In vivo detection of egfr mutation in glioblastoma via mri signature consistent with deep peritumoral infiltration

ABSTRACT

A method, including a computer-implemented method, is provided for in vivo detection of epidermal growth factor receptor (EGFR) mutation status within peritumoral edematous tissue of a patient. The method includes performing quantitative pattern analysis of magnetic resonance imaging (MRI) data corresponding to MRI of in vivo peritumoral edematous tissue to determine a level of spatial heterogeneity or similarity within the in vivo peritumoral edematous tissue. EGFR mutation status is assigned as one of negative or positive based on the level of spatial heterogeneity or similarity determined. A non-transitory computer-readable storage medium and a system are also provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/493,722, filed Apr. 21, 2017, which claims the benefit of U.S.Provisional Patent Application No. 62/484,034, filed Apr. 11, 2017, andU.S. Provisional Patent Application No. 62/325,764, filed Apr. 21, 2016,all of which are hereby incorporated in their entireties.

STATEMENT OF FEDERALLY SPONSORED RESEARCH

This invention was made with government support from grant R01-NS042645awarded by the National Institutes of Health (NIH) and grantU24-CA189523 awarded by the NIH. The US government has certain rights inthis invention.

BACKGROUND

Glioblastoma (GBM) is the most common and aggressive primary malignantadult tumor of the central nervous system. GBM may be located anywherein the brain or spinal cord, but is typically found in the cerebralhemispheres of the brain. According to the American Brain TumorAssociation, GBMs represent about 15.4% of all primary brain tumors andabout 60-75% of all astrocytomas [American Brain Tumor Association,Glioblastoma, Webpagewww.abta.org/brain-tumor-information/types-of-tumors/glioblastoma.html,(2014), Last accessed: Apr. 1, 2016].

GBMs have average survival of 14 months [D. Johnson, B. O'Neill,Glioblastoma survival in the United States before and during thetemozolomide era. Journal of Neuro-Oncology 107, 359-364 (2011)]following standard treatment with surgical resection andchemo-radiation, and 4 months otherwise. Although these treatmentoptions have improved over the years, there has not been any substantialimprovement in the overall survival rates of GBM patients.

GBM is known to have highly heterogeneous expression of molecularcharacteristics. The effect of the above referenced treatment optionshas been hindered and confounded by the GBM heterogeneity, which isevident at the radiological, cellular and genetic level [J.-M. Lemee, etal, Intratumoral heterogeneity in glioblastoma: don't forget theperitumoral brain zone. Neuro-Oncology 17, 1322-1332 (2015)].Determining the highly heterogeneous molecular characteristics inpatients diagnosed with GBM is challenging, as conventional options arebased on analysis of tissue, which is invasive and, most importantly,fails to capture the spatial heterogeneity of gene expression, as it isbased on an analysis of a localized tissue sample.

The epidermal growth factor receptor (EGFR) is a regulator of normalcellular growth in tissues of epithelial origin [H. Gan et al., Theepidermal growth factor receptor variant III(EGFRvIII): where the wildthings are altered. FEBS Journal 280, 5350-5370 (2013)], and has beenwell-validated as a target for cancer therapy. Overexpression of thecell-surface EGFR leads to deregulation in its signaling, which is amain contributor to the formation of many epithelial malignancies inhumans [P. Humphrey, et al, Anti-synthetic peptide antibody reacting atthe fusion junction of deletion-mutant epidermal growth factor receptorsin human glioblastoma. Proc Natl Acad Sci USA 87, 4207-4211 (1990); D.Salomon, et al., Epidermal growth factor-related peptides and theirreceptors in human malignancies. Crit Rev Oncol Hematol 19, 183-232(1995)]. In such cases, which is the majority of patients withhigh-grade gliomas [A. B. Heimberger, et al., The natural history ofEGFR and EGFRvIII in glioblastoma patients. Journal of TranslationalMedicine 3, (2005a)], there is typically an associated geneamplification [C. Arteaga, Epidermal growth factor receptor dependencein human tumors: more than just expression? Oncologist 7, 31-39 (2002)]or mutation [R. Nishikawa, et al, A mutant epidermal growth factorreceptor common in human glioma confers enhanced tumorigenicity. ProcNatl Acad Sci USA 91, 7727-7731 (1994)] in EGFR [C. W. Brennan, et al,The Somatic Genomic Landscape of Glioblastoma. Cell 155, 462-477(2013).]. Furthermore, mutations in EGFR have been identified asprimarily occurring in one particular molecular subtype of GBM, namelyclassical [H. S. Phillips, et al. Molecular subclasses of high-gradeglioma predict prognosis, delineate a pattern of disease progression,and resemble stages in neurogenesis. Cancer Cell 9, 157-173 (2006)], andcorresponding “abnormalities” in EGFR expression have been associatedwith poorer survival and reduced response to aggressive therapy [R.Verhaak, et al, Integrated Genomic Analysis Identifies ClinicallyRelevant Subtypes of Glioblastoma Characterized by Abnormalities inPDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98-110 (2010)]. The mostcommon extracellular EGFR mutation is the variant III (EGFRvIII), whichis an important factor in driving tumor progression and definingprognosis in GBM patients [N. Shinojima, et al, Prognostic value ofepidermal growth factor receptor in patients with glioblastomamultiforme. Cancer Research 63, 6962-6970 (2003); K. D. Aldape, et al,Immunohistochemical detection of EGFRvIII in high malignancy gradeastrocytomas and evaluation of prognostic significance. Journal ofNeuropathology and Experimental Neurology 63, 700-707 (2004); A. B.Heimberger, et al, Prognostic effect of epidermal growth factor receptorand EGFRvIII in glioblastoma multiforme patients. Clinical CancerResearch 11, 1462-1466 (2005b)]. Half of EGFR-amplified tumors harborthe EGFRvIII mutation, which is a gene rearrangement due to in-framedeletion of exons 2-7 from this receptor tyrosine kinase [H. K. Gan, etal. The EGFRvIII variant in glioblastoma multiforme. Journal of ClinicalNeuroscience 16, 748-754 (2009)]. This deletion consequently causesconstitutive signaling in the absence of ligand binding [Q.-W. Fan, etal., EGFR Phosphorylates Tumor-Derived EGFRvIII Driving STAT3/5 andProgression in Glioblastoma. Cancer Cell 24, 438-449 (2013]. In contrastto EGFR, which can be found in normal tissue, EGFRvIII is expressed onlyin cancerous tissue [J. H. Sampson, et al., Tumor-specific immunotherapytargeting the EGFRvIII mutation in patients with malignant glioma. SeminImmunol 20, 267-275 (2008)], can be found nearly in 33% of GBM patients[Shinojima, cited above; Aldape, cited above; Heimberger, cited above]and its overexpression worsens the prognosis [Shinojima, cited above;Heimberger, 2005a, cited above; Heimberger, 2005b, cited above].Specifically, patients harboring the mutation (EGFRvIII-positive) haveshown significantly shorter median OS [J. H. Sampson, et al., Journal ofClinical Oncology 28, 4722-4729 (2010)]. EGFRvIII is also associatedwith activation of numerous oncogenic processes leading to aggressivetumor growth and proliferation [Nishikawa, cited above; M.-d.-M. Inda,et al, Tumor heterogeneity is an active process maintained by a mutantEGFR-induced cytokine circuit in glioblastoma. Genes & Development 24,1731-1745 (2010); A. Porter, A dead end: a review of glioblastomamultiforme. Eukaryon 8, 64-68 (2012)], hence evidence of the mutant'spresence can have a very high impact on treatment decisions, as well ason evaluating treatment response. For these reasons, vaccination againstEGFRvIII is a potentially promising immunotherapy [J. H. Sampson, etal., Tumor-specific immunotherapy targeting the EGFRvIII mutation inpatients with malignant glioma. Semin Immunol 20, 267-275 (2008); J. H.Sampson, et al. Immunologic escape after prolonged progression-freesurvival with epidermal growth factor receptor variant III peptidevaccination in patients with newly diagnosed glioblastoma. Journal ofClinical Oncology 28, 4722-4729 (2010)], and EGFRvIII represents apotentially viable therapeutic target for GBM patients [B. Kalman, etal., Epidermal growth factor receptor as a therapeutic target inglioblastoma. Neuromolecular medicine 15, 420-434 (2013); I. Veliz, etal., Ann Trans Med 3, 7 (2015)] that has been the target of severalinvestigational drug trials and pilot studies [D. O'Rourke, S. Chang,Pilot Study of Autologous T Cells Redirected to EGFRVIII—With a ChimericAntigen Receptor in Patients With EGFRVIII+ Glioblastoma(ClinicalTrials.gov Identifier: NCT02209376) (2014); D. O'Rourke, etal., Neuro Oncol 17, v110-v111 (2015); Celldex, A Study ofRindopepimut/GM-CSF in Patients With Relapsed EGFRvIII-PositiveGlioblastoma (ReACT) (ClinicalTrials.gov Identifier: NCT01498328).(2011); Celldex, Phase III Study of Rindopepimut/GM-CSF in Patients WithNewly Diagnosed Glioblastoma (ACT IV) (ClinicalTrials.gov Identifier:NCT01480479). (2011)].

Although determination of EGFRvIII status is vital for targetedtherapeutics in GBM, invasive studies are required for currenttissue-based approaches, which include immunohistochemistry and nextgeneration sequencing. The process of such approaches is hindered by thespatial [A. Sottoriva, et al. Intratumor heterogeneity in humanglioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad SciUSA 110, 4009-4014 (2013); A. P. Patel, et al. Single-cell RNA-seqhighlights intratumoral heterogeneity in primary glioblastoma. Science344, 1396-1401 (2014)] and temporal heterogeneity [P. C. Gedeon, et al.Rindopepimut: anti-EGFRvIII peptide vaccine, oncolytic. Drugs Future 38,147-155 (2013); M. J. v. d. Bent, et al. Changes in the EGFRamplification and EGFRvIII expression between paired primary andrecurrent glioblastomas Neuro-Oncology 17, 935-941 (2015); S. P. Niclou,Gauging heterogeneity in primary versus recurrent glioblastoma.Neuro-Oncology 17, 907-909 (2015)] of molecular alterations within theGBM tumor that give rise to sampling error (i.e., analysis of singletissue specimen is not sufficient for determining the dominant mutantexpression). Furthermore, the invasive nature of repeated biopsies makesit nearly impossible to evaluate the dynamic equilibrium of mutationsand molecular characteristics that occur during the course of treatment,hence adapt the treatment accordingly. Patient stratification andselection for treatment is limited for the same reason. In other cases,the biopsy (or resection) of the tumor might not always be possible,such as in cases of deep-seated tumors, in which there is no sufficientsample size for histopathological analysis. Finally, molecular testingmay be unavailable in certain clinical settings due to cost or equipmentavailability.

Thus, accurate, reproducible, non-invasive methods for diagnosis ofcancers associated with EGFRvIII mutations are needed.

SUMMARY OF THE INVENTION

In one aspect, a computer-implemented method for in vivo detection ofepidermal growth factor receptor EGFR mutation status is provided,utilizing Magnetic Resonance Imaging (MRI) signals within peritumoraledematous tissue. Such mutations may include tumors (e.g., glioblastoma)overexpressing the wild-type EGFR (mutational activation), and/or tumorsharboring EGFR splice variant III (EGFRvIII+), and/or tumors harboringpoint mutations such as A289V, G598V, or R108K. The computer-implementedmethod includes, executing on a processor, the step of performingquantitative pattern analysis of MRI data to determine a level ofspatial heterogeneity or similarity within the in vivo peritumoraledematous tissue and the step of assigning EGFR mutation status as oneof negative or positive (EGFRvIII+ or EGFRVIII−, EGFR A298V+/−; EGFRG598V+/−, or EGFR R108K+/−_) based on the level of spatial heterogeneityor similarity determined.

In another aspect, a method of in vivo detection of EGFR mutation statuswithin the peritumoral edematous tissue of a patient is provided. Thismethod includes; a) acquisition of MRI data corresponding to in vivoperitumoral edematous tissue of a patient, b) identification ofseparate, non-overlapping first and second regions of interest (ROIs)within the peritumoral edematous tissue, c) analysis of the MRI datacorresponding to the separate first and second ROIs to determine a levelof heterogeneity or similarity therebetween, and d) assigning EGFRmutation status as one of negative or positive based on the level ofheterogeneity or similarity determined.

In a further aspect, the methods and systems described herein are usefulin monitoring the progress of a patient following initiation oftreatment (e.g., post-dosing with a specific EGFR-targeted or anon-targeted therapy. The system allows detection of one or more of:reduced tumor size, reduced peritumoral edematous tissue, and/or reducedpresence of tissue having an EGFR mutation. In certain embodiments, oneor more of these may be observed in the absence of any change inanother.

In a further aspect, a non-transitory computer-readable storage mediumis provided and includes stored instructions which, when executed by oneor more computer processors, cause the one or more computer processorsto perform quantitative pattern analysis of MRI data corresponding tothe peritumoral edematous tissue, in order to determine a level ofspatial heterogeneity or similarity within the peritumoral edematoustissue, and to assign EGFR mutation status as one of negative orpositive based on the level of spatial heterogeneity or similaritydetermined.

In yet a further aspect, a system for in vivo detection of EGFR mutationstatus within the peritumoral edematous tissue of a patient is provided.The system includes at least one processor configured to performquantitative pattern analysis of MRI data corresponding to MRI of invivo peritumoral edematous tissue to determine a level of spatialheterogeneity or similarity within the peritumoral edematous tissue. Theat least one processor is being configured to assign EGFR mutationstatus as one of negative or positive, based on the level of spatialheterogeneity or similarity determined.

BRIEF DESCRIPTION OF THE DRAWINGS

Various features of the embodiments described in the following detaileddescription can be more fully appreciated when considered with referenceto the accompanying figures, wherein the same numbers refer to the sameelements.

FIGS. 1A-1D show distributions of the Peritumoral Heterogeneity Index(PHI, or φ-index) for various alterations in the Epidermal Growth FactorReceptor (EGFR), as measured in baseline pre-operative scans of de novoglioblastoma. In FIG. 1A, values for the wild-type EGFR (wt-EGFR, i.e.,“the controls”) are much higher than the values obtained for variousEGFR alterations, including tumors harboring the splice variant III(vIII+), as well as those harboring various point mutations (i.e.,A289V, G598V, R108K). In FIG. 1B shows another wt-EGFR cohortidentified, independent from the one shown in FIG. 1A, with quantitativeexpression levels available. FIG. 1C shows the clear distinction, basedon PHI, between highly-expressed wt-EGFR (high-expr) and those withlower expression. FIG. 1D shows a scatter plot of all samples includedin FIG. 1C, pinpointing the relation between the wt-EGFR expressionlevel and the obtained PHI (Corr=−0.48, p=0.0047).

FIGS. 2A and 2B are examples of immediate and distant peritumoral regionof interest (ROI) annotations. FIG. 2A illustrates a near ROI (16)defined adjacent to the enhancing part (12) of the tumor (10)superimposed on a T1-CE axial image, and FIG. 2B illustrates a far ROI(18) defined in the periphery of the tumor (10) within edema (14)superimposed on a T2-FLAIR axial image. These ROIs are described bylines annotated in multiple slices for each subject and not just in asingle slice, as shown in this visual example.

FIGS. 3A-3F show the temporal perfusion dynamics for the describedimmediate and distant peritumoral regions of interest (ROIs), byEGFRvIII expression status. FIGS. 3A and 3B illustrate examples ofaligned average perfusion curves for individual patients. FIGS. 3C and3D show the summarization of perfusion curves through principalcomponent analysis in three components. Note that each perfusion curvein FIG. 3A and FIG. 3B is represented by a single dot in FIG. 3C andFIG. 3D. These summarized perfusion curves show more separability(higher φ-index) between the immediate and the distant peritumoral ROImeasures among EGFRvIII-patients compared to EGFRvIII+ patients. FIGS.3E and 3F illustrate aligned average perfusion curves across 142patients. Note that the drop in the perfusion signal for the distantperitumoral ROI is almost identical between across all patients, andthat the average drop in the immediate peritumoral ROI is much deeperamong the EGFRvIII-patients compared to EGFRvIII+ patients.

FIGS. 4A-4C show distributions of the Peritumoral Heterogeneity Index(PHI) by EGFRvIII expression status across the discovery cohort in FIG.4A, the replication cohort in FIG. 4B, and the combined cohort in FIG.4C. Statistical significance was evaluated via a two-tailed pairedt-test comparing between the two distributions of: the discovery cohort(p=1.5725×10-7) (FIG. 4A), the replication cohort (p=2.8164×10-4) (FIG.4B), and the combined cohort (p=4.0033×10-10) (FIG. 4C). The bottom andtop of each “box” depict the 1st and 3rd quartile of the PHI measure,respectively. The line within each box indicates the median, and thefact that it is not necessarily at the center of each box indicates theskewness of the distribution over different cases. The “whiskers” drawnexternal to each box depict the extremal observations still within 1.5times the interquartile range, below the 1st or above the 3rd quartile.Observations beyond the whiskers are marked as outliers with a “+” sign.

FIGS. 5A-L provides Receiver Operating Characteristics (ROC) Analyses.FIGS. 5A—SI provide ROC curves for individual MRI modalities across 140patients. FIGS. 5J-5K provide ROC curves for combination of modalitiesacross 140 patients. FIG. 5I provides ROC curves for a desirableembodiment of the invention in 142 patients.

FIGS. 6A-6B are scatter plots of the Peritumoral Heterogeneity Index(PHI), by EGFRvIII expression status across 140 patients, in the DSCmodality (x axis) over the PHI in the DTI-TR measure (y axis) in FIG. 6Aand over the PHI in the T1 modality (y axis) in FIG. 6B.

FIG. 7 is a scatter plot of the DTI-TR measure, by EGFRvIII expressionstatus across 140 patients, for the region adjacent to the enhancingtumor (x axis) over the region at the periphery of the edema (y axis).

FIG. 8 is a scatter plot of the Peritumoral Heterogeneity Index (PHI) iny axis over the median Euclidean distance between the near and far ROIfor each patient, by EGFRvIII expression status across 142 patients. PHIis uncorrelated to the distance of the ROIs (correlation coefficient:0.0519, p-value=0.5394).

FIG. 9 shows the distributions of the median Euclidean distance betweenthe near and far ROIs for each patient according to their EGFRvIIIstatus. There is no significant difference between the two groups(p-value=0.6728).

FIGS. 10A and 10B illustrate leakage corrected rCBV distributions foreach ROI of each subgroup. FIG. 10A provides the EGFRvIII+ subgroup.FIG. 10B provides the EGFRvIII-subgroup. Although these rCBV results areconsistent with those obtained via PHI, the separation between EGFRvIII+and EGFRvIII− is notably weaker.

DETAILED DESCRIPTION OF THE INVENTION

For simplicity and illustrative purposes, the principles of theembodiments are described by referring mainly to examples thereof. Inthe following description, numerous specific details are set forth inorder to provide a thorough understanding of the embodiments. It will beapparent however, to one of ordinary skill in the art, that theembodiments may be practiced without limitation to these specificdetails. In some instances, well known methods and structures have notbeen described in detail so as not to unnecessarily obscure theembodiments.

As used herein, the term “glioma” refers to a type of tumor that occursin the brain and spinal cord. Gliomas begin in the gluey supportivecells (glial cells) that surround nerve cells and help them function.

As used herein, the term “Glioblastoma (GBM)” refers to the most commonand aggressive primary malignant adult tumor of the central nervoussystem. Glioblastoma may be located anywhere in the brain or spinalcord, but is typically found in the cerebral hemispheres of the brain.

As used herein, the term “Epidermal Growth Factor (EGF)” refers to atyrosine protein kinase whose receptor over-expression is one of thehallmarks of glioblastoma, present in 50-60% of tumors [A. B.Heimberger, et al. The natural history of EGFR and EGFRvIII inglioblastoma patients, Journal of Translational Medicine 3, (2005)]. Asused herein, the term “Epidermal Growth Factor Receptor (EGFR)” refersto a protein found on the surface of cells to which epidermal growthfactor (EGF) binds. EGFR is a regulator of normal cellular growth intissues of epithelial origin. The sequence of human EGFR is available,e.g., uniprot.org/uniprot/P00533.

As used herein, the term “Epidermal Growth Factor Receptor mutant”refers to any of the EGFR mutations which is associated with a diseaseor disorder. Amplification of wtEGFR may be observed in addition to oneor more of mutations. In certain embodiments, wtEGFR amplification isobserved in the absence of mutations. Examples of human EGFR mutantsinclude EGFR A298 mutants (including changes from the Ala at position298 to one of V (A298V+/−), D (A298D+/−), T (A298T+/−), N (A298N+/−), orI (A298I+/−); EGFR G598V+/−, or EGFR R108 (including changes from theArg at position 108 to K (R108K+/−) or R108G+/−. In one embodiment, theEGFR mutant is variant III (EGFRvIII)”, alternatively named de2-7 EGFRor ΔEGFR, refers to the most common extracellular EGFR mutation, whichlacks the extracellular ligand binding domain of exons 2-7. See, e.g., HK Gan et al, FEBS J, 2013 November; 280(2):5350-60. EGFRvIII is animportant factor in driving tumor progression and in defining prognosisin cancer patients, and therefore, provides a potential therapeutictarget. This EGFRvIII mutation has been described as being present inGBM (a brain cancer). However, the EGFRvIII is also present in othercancers, for which the techniques and apparatus described herein arealso useful. Such cancers may include, e.g., EGFRvIII-associatedmetastatic prostate cancer, breast cancer, anaplastic astrocytoma, lungcancers (non-small cell lung cancer, predominantly squamous cell type),adenocarcinomas, head/neck cancers, thyroid cancers, bladder cancer,ovarian cancer, and colorectal cancer. Other EGFR-mutants and theirassociated conditions are known to those of skill in the art. See, e.g.,uniprot.org/uniprot/P00533. This sequence is reproduced in SEQ ID NO: 1for convenience. The residue numbers of the mutations described hereinis provided with reference to the numbering of this sequence, whichincludes the signal peptide. In certain embodiments, a patient may havea neoplasm associated with combinations of one or more of these mutants.

As used herein, the term “resection” refers to tumor tissue removal viasurgery.

As used herein, the term “edema” refers to swelling of a selectedtissue, e.g., in brain tissue a common result of having a brain tumor.The term “peritumoral edema” refers to edema occurring around a tumor,typically depicted by high signal intensity in T2-weightedfluid-attenuated inversion recovery (T2-FLAIR) MRI.

As used herein, the term “spatial heterogeneity”, or else the spatialgradient as one moves away from the tumor, refers to an unevendistribution or variability of a characteristic across peritumoraledematous tissue, and is measured by the Bhattacharyya distance betweenthe signals in a near-tumor regions of interest (ROI) and the signals ina far-from-the-tumor edematous ROI.

As used herein, the term “perfusion” refers to the steady-state deliveryof blood to a capillary bed in its biological tissue. Perfusion isvariably used for different physiologic parameters that also affect theimaging (e.g., magnetic resonance) signal, e.g., blood volume, bloodvelocity, and blood oxygenation.

As used herein, the term “neovascularization” refers to the formation offunctional microvascular networks with red blood cell perfusion.

As used herein, the term “Magnetic Resonance Imaging (MRI)” refers to amedical imaging technology using radio waves and a magnetic field tocreate detailed images of organs and tissues, such as brain tissue. Theworking examples herein illustrate use of dynamic susceptibilitycontract (DSC) MRI perfusion images. However, the invention is not solimited, and Dynamic Contrast Enhanced (DCE) MRI perfusion images couldalso be considered. Furthermore, signal from other MRI modalities couldalso be added (or in place of the perfusion images), such as T1-weighted(pre- and post-contrast), T2-weighted (pre- and post-contrast). Althoughthe examples use the 3-Tesla Siemens Magentom Tria A Tim clinical MRIsystem using our standard clinical protocol and settings (Erlangen,Germany), it will be readily understood that by one of skill in the artthat the process and apparatus described herein are not limited to thisequipment, settings, sequences, or imaging. For example, a variety ofsuitable imaging systems are commercially available, e.g., from Siemens,GE, Philips, Hitachi or Toshiba. The apparatus provided herein may beintegrated with the imaging apparatus, or separate from, and operablylinked so that the data from the imaging apparatus is transmittedelectronically to apparatus described herein. Alternatively, theprocessor(s) described herein are not directly operably linked toreceive the data output of the imaging system, but is a stand-alonesystem to which the data are delivered by separate means. The manner inwhich the starting data are delivered to a processor as describedherein, is not a limit on the present invention.

The term “ROI” or “region of interest” refers to a selected subset ofsamples within a data set identified for a specific purpose, e.g., theboundaries of a tumor as defined on an image or in a volume, for thepurpose of measuring its size/volume.

The term “voxel” refers to each of an array of elements of volume thatconstitute a notional three-dimensional space, as customary in anyimaging modality.

As used herein, the term “processor” refers to a functional unit thatinterprets and executes instruction data. Such a functional unit may be,e.g., a computer, a diffusion data apparatus, a hand-held device orother apparatus or equipment.

As described herein, computer software may be operably linked to aprocessor or other apparatus.

As used herein, the term “Dynamic Susceptibility Contrast (DSC)-MRI”refers to an MRI capable of assessing cerebral microvasculature. InDSC-MRI perfusion imaging, a contrast agent is injected into the bloodand monitored as it passes through the microvasculature.

As used herein, T1 refers to the time constant (usually reported inmilliseconds, msec) during the T1 relaxation. Relaxation meansrestoration of the equilibrium state or going back to a low-energy levelafter excitation. T1 relaxation is the process by which the longitudinalmagnetization is recovered (after the excitation pulse is applied) dueto transfer of energy from the nuclear spin system to the neighboringmolecules (the lattice). It occurs in the z-direction (z-axis is oftendepicted as a vertical line). The T1 relaxation time is a measure of therate of transfer of energy from the nuclear spin system to theneighboring molecules (the lattice). It is the time when 63% of thelongitudinal magnetization has recovered.

The term “T1 weighted image” refers to one of the basic pulse sequencesin MRI and demonstrates differences in the T1 relaxation times oftissues. T1-weighted imaging is used to differentiate anatomicalstructures mainly on the basis of T1 values; i.e. the scanningparameters are set (short repetition time (TR)/short echo time (TE)).Tissues with high fat content (e.g. white matter) appear bright andcompartments filled with water (e.g. CSF) appears dark.

As used herein, T2 relaxation is the process by which the transversemagnetization decays due to dephasing of proton spins (spins becomingdesynchronized). After the excitation pulse is applied, themagnetization flips 90 degrees from the longitudinal axis to thexy-plane. The transverse magnetization is initially maximum (due tocoherent nuclear spins) but this arrangement is gradually lost due tofield inhomogeneities and/or direct interactions between the spins(without energy transfer to the lattice). T2 relaxation occurs on thexy-plane and is often depicted as the spreading of magnetic momentsalong the plane. The T2 relaxation time is a measure of the rate of thedecay of transverse magnetization within the xy-plane. It is the timewhen 63% of the transverse magnetization has decayed. The term“T2-FLAIR” refers to a T2 weighted fluid-attenuated inversion recoverymagnetic resonance image. See, e.g., De Coene B, et al. American Journalof Neuroradiology, 13(6):1555-1564 (1992); and Hajnal J V, et al. JComput Assist Tomogr 1992; 16:841-844.

Other weighting techniques, such as diffusion-weighting and perfusionweighting are also known in the art.

As used herein, the term “Principal Component Analysis (PCA)” is amathematical and statistical procedure that transforms a number ofpossibly correlated variables into a smaller number of uncorrelatedvariables called principal components. See [H. Hotelling, Analysis of acomplex of statistical variables into principal components, Journal ofEducational Psychology, 24(6):417-441 (1933); K. Pearson, On Lines andPlanes of closest fit to systems of points in space, PhilosophicalMagazine, 2(11):559-572 (1901)] See, e.g., Jolliffe I. T. PrincipalComponent Analysis, Series: Springer Series in Statistics, 2nd ed.,Springer, N Y, 2002, XXIX, 487 p. 28 illus. ISBN 978-0-387-95442-4. See,also, J. Schlens, “A Tutorial on Principal Component Analysis”,arxiv.org/pdf/1404.1100v1.pdf (Apr. 7, 2014).

As used herein, the term “Bhattacharyya Coefficient” is a statisticalmeasure of the amount of overlap between two statistical samples orpopulations. See, A. Bhattacharyya, Bulletin of the CalcuttaMathematical Society, 35: 99-109 (1943).

As used herein, the terms “Peritumoral Heterogeneity Index (PHI)” or“φ-index” are used to refer to a measure of the separability betweensummarized perfusion measurements of two ROIs.

It is to be noted that the term “a” or “an” refers to one or more. Assuch, the terms “a” (or “an”), “one or more,” and “at least one” areused interchangeably herein.

The words “comprise”, “comprises”, and “comprising” are to beinterpreted inclusively rather than exclusively. The words “consist”,“consisting”, and its variants, are to be interpreted exclusively,rather than inclusively. While various embodiments in the specificationare presented using “comprising” language, under other circumstances, arelated embodiment is also intended to be interpreted and describedusing “consisting of” or “consisting essentially of” language.

As used herein, the term “about” means a variability of 10% from thereference given, unless otherwise specified.

A “patient” is a mammal, e.g., a human, mouse, rat, guinea pig, dog,cat, horse, cow, pig, or non-human primate, such as a monkey,chimpanzee, baboon or gorilla. In one embodiment, the patient is ahuman.

Unless defined otherwise in this specification, technical and scientificterms used herein have the same meaning as commonly understood by one ofordinary skill in the art and by reference to published texts, whichprovide one skilled in the art with a general guide to many of the termsused in the present application.

Embodiments

Embodiments disclosed herein use EGFR mutants as a target for cancertherapy. For instance, EGFR events can be identified to drive oneparticular molecular subtype of GBM, namely classical GBM, andcorresponding “abnormalities” in the EGFR expression may be associatedwith predicting survival rate and with directing response and treatmenttoward aggressive therapies.

Conventionally, EGFR mutant status is determined based on invasivetissue-based approaches, which include immunohistochemistry and nextgeneration sequencing. These processes are hindered by numerousintrinsic and extrinsic factors. First, the expression of molecularcharacteristics in GBMs is spatially heterogeneous [A. Sottoriva, et al.Intratumor heterogeneity in human glioblastoma reflects cancerevolutionary dynamics. Proc Natl Acad Sci USA 110, 4009-4014 (2013); A.P. Patel, et al. Single-cell RNA-seq highlights intratumoralheterogeneity in primary glioblastoma. Science 344, 1396-1401 (2014)];and hence, a single tissue might not be sufficient for determining thedominant expression of the mutant. Second, molecular characteristics ofGBM are temporally heterogeneous [P. C. Gedeon, et al. Rindopepimut:anti-EGFRvIII peptide vaccine, oncolytic. Drugs Future 38, 147-155(2013); M. J. v. d. Bent, et al. Changes in the EGFR amplification andEGFRvIII expression between paired primary and recurrent glioblastomasNeuro-Oncology 17, 935-941 (2015); S. P. Niclou, Gauging heterogeneityin primary versus recurrent glioblastoma. Neuro-Oncology 17, 907-909(2015)], i.e., they change their expression status over time both due todisease progression/recurrence and with treatment. This, combined withthe fact that repeated biopsies are not considered due to their invasivenature, makes it nearly impossible to evaluate the expression dynamicsof molecular characteristics during treatment, and hence, to adapttreatment accordingly. Patient stratification and selection intotreatments is limited for the same reason. Third, the biopsy (orresection) of the tumor might not always be possible, such as in casesof deep-seated tumors, where there is insufficient sample size forhistopathological assessment and analysis. Finally, retrieval of suchmolecular characteristics may be unavailable in certain clinicalsettings, as it requires costly and not widely-available equipment fortissue-based genetic testing, especially when such testing must beperformed repeatedly for treatment monitoring.

According to embodiments disclosed herein, the above referenced problemsare overcome with a method of quantitative analysis of spatialheterogeneity of peritumoral heterogeneity in magnetic resonance (MR)signals. By way of example and in accordance to one embodiment, themethod is directed to quantitative analysis of spatial heterogeneity ofperitumoral perfusion imaging dynamics. Accordingly, the analysisperformed based on embodiments may be used to construct a non-invasiveimaging biomarker of the EGFR mutation status, an important moleculartarget in GBM as discussed above. Thus, the status of EGFR may bedetermined based solely on quantitative MRI phenotypes. Suitably, thewithin-patient peritumoral heterogeneity index (PHI/φ-index, is used tocontrast perfusion patterns of immediate and distant peritumoral edema.One suitable program for use in this method is available on:med.upenn.edu/sbia/phiestimator.html, which is hereby incorporated byreference in its entirety.

The above referenced in vivo imaging marker can be particularly usefuland important in the preoperative evaluation of the mutant, enablingdecisions on the aggressiveness of the resection, and in cases involvingrecurrent tumors without baseline status and in evaluating the dominant(i.e., global) expression of the mutant, instead of conventionalconsideration of a single tissue specimen. The disclosed imaging andimaging analysis may also prove helpful in patient selection forclinical trials based on the global (i.e. more unified) expression ofmutation status and in retrieving the mutation status in inoperable(e.g. deep-seated) tumors. In addition, the disclosed process may beused to provide a spatial map of EGFRvIII expression, rather than aglobal measure, and such information may be used to guide more targetedsurgical resection and radiation (e.g. with protons).

Accordingly, the methods and techniques disclosed herein provide arobust, reproducible, non-invasive, and clinically relativelyeasy-to-perform evaluation of an imaging signature of the EGFRvIIIexpression, for instance, in a GBM. Due to the non-invasive nature ofthe disclosed technique, use of the in vivo imaging marker may beparticularly important in cases of: i) pre-operative evaluation of themutant, guiding resection aggressiveness; ii) recurrent tumors withoutbaseline status; iii) evaluating the dominant (i.e., global) expressionof the mutant, instead of considering a single tissue specimen; iv)patient selection for clinical trials based on the global (i.e., moreunified) expression of the mutation status; v) retrieving the mutationstatus in inoperable (e.g., deep-seated tumors); and vi) post-operativecontinuous/repeated monitoring of the status of the mutant, henceassisting in dynamically adapting the applied treatment.

The specific imaging signature of EGFRvIII expression may be based onquantitative pattern analysis of clinically used MRI perfusion or otherimages. The signature has been derived and validated from patients withde novo GBM. More specifically, the imaging signature of EGFRvIII may bedirectly derived from peritumoral heterogeneity in edematous regionsthat is consistent with the highly infiltrative nature ofEGFRvIII-positive tumors, which are associated with tumor-like perfusionpatterns far away from the bulk-tumor, in contrast to EGFRvIII-negativetumors. Further, the EGFRvIII imaging signature is constructed in amanner that is very robust to MR scanner variations, by virtue ofevaluating within-patient heterogeneity measures, rather than relying ona patient-wide universal threshold.

Accordingly, embodiments disclosed herein utilize a robust andreproducible in vivo marker of the important above referenced mutationthat can be obtained from quantitative analysis of standard clinicalimages, thereby facilitating non-invasive patient selection for targetedtherapy, stratification for clinical trials, prognosis, and repeatablemonitoring of the EGFRvIII mutation status during the treatment courseand at the time of recurrence in clinical settings. Further, because ofthe heterogeneity of GBM and the results achieved with differenttreatment responses, personalized/precision medicine may be more readilyutilized and new treatment options may be adopted based on the abovereferenced targeting of specific molecular characteristics.

Embodiments disclosed herein take into consideration that peritumoraledema occurs as a result of infiltrating tumor cells, as well as abiological response to the angiogenic and vascular permeability factorsreleased by the spatially adjacent tumor cells [E. L. Chang, et al.Evaluation of peritumoral edema in the delineation of radiotherapyclinical target volumes for glioblastoma. International Journal ofRadiation Oncology, Biology, Physics 68, 144-150 (2007); H. Akbari, etal. Pattern analysis of dynamic susceptibility contrast-enhanced MRimaging demonstrates peritumoral tissue heterogeneity. Radiology 273,502-510 (2014)]. EGFRvIII-positive tumors, which are very aggressive andinfiltrative, present imaging signatures consistent with uniformly densedistribution of tumor cells throughout the peritumoral edematous tissue.Conversely, tumors lacking the mutation (i.e., EGFRvIII-negative) have adecreasing/sparser tumor cell burden with increasing distance from theenhancing part of the tumor. Therefore, an assessment of the tumor cellinfiltration heterogeneity in peritumoral edematous tissue isdiscriminatory with respect to EGFRvIII mutation status, and therebyprovides a distinctive imaging biomarker in the embodiments.

Although most of the attention in characterizing tumors has beenconventionally placed on the tumor itself, the peritumoral edematousregion, typically depicted by high T2-FLAIR signal intensity of MRimaging, is submitted as being potentially more important. Despite thefact that more than 90% of tumor recurrences occur in edema due to thehighly infiltrative nature of GBM, there is limited research focused inthe assessment of this region and its microenvironment [J.-M. Lemee, etal. Intratumoral heterogeneity in glioblastoma: don't forget theperitumoral brain zone. Neuro-Oncology 17, 1322-1332 (2015); R. Jain, etal. Outcome Prediction in Patients with Glioblastoma by Using Imaging,Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component ofthe Tumor. Radiology 272, 484-493 (2014)].

As stated above, peritumoral edema results from infiltrating tumor cellsor is simply the biological response to the angiogenic and vascularpermeability factors released by the spatially adjacent tumor cells.Although the edematous region surrounding the tumor bulk remains mostlyunresected and is generally not aggressively treated, by virtue of beingthe tumor's “propagating font” is critically important for diagnosticand therapeutic purposes.

Existing healthy/normal blood vessels can provide oxygen and nutrientsupplies sufficient for tumors in early stages, but not enough forlarger tumors. This results in the phenomenon of ischemia, secretion ofangiogenic factors and cytokines that eventually lead to proliferationof new vessels (i.e., neovascularization, increased permeability andedema. These new vessels, when compared with the existing healthy bloodvessels, have increasingly tortuous and branched structure, as well asmore permeability, which may affect brain circulation.

Alterations in brain circulation can be captured by the dynamicsusceptibility contrast material-enhanced magnetic resonance imaging(DSC-MRI) modality, which is based on the decay of T2 or T2-STAR signalduring the first pass of a paramagnetic contrast medium through thecapillary bed. For example, EGFRvIII-positive tumors, compared to lessaggressive EGFRvIII-negative tumors, have uniformly increased cellsthroughout regions of peritumoral edema that are associated withneovascularization and are readily detectable by analysis of DSC-MRI.Therefore, DSC-MRI enables the generation of a perfusion curve byassessing the dynamic changes in the brightness intensity of a specificregion through time. Analysis of this perfusion temporal dynamicinformation through Principal Component Analysis (PCA) enablesmicrovascular imaging and provides a visual correlation of blood flow,blood volume, and vessel permeability. Examples of temporal perfusiondynamic curves and their summarization through PCA are shown in FIGS.3-6 discussed below in greater detail.

In accordance to one embodiment, dimensionality reduction methods, suchas PCA, are used to analyse and classify tissue from time-series imagesof perfusion MR images (such as DSC-MRI) of patients with GBM. Here, PCAis utilized for purposes of analyzing substantially the whole timeseries. In addition, application of machine-learning tools (such as,Support Vector Machine, SVM) on the dimensionality-reduced data from theDSC MR images may be able to provide a robust identification of tissuecharacteristics for the region of tissue being analysed. The abovereferenced analysis has been found to uncover peritumoral region tissuecharacteristics that are clinically important and newly able to becaptured by embodiments disclosed herein.

According to one embodiment, peritumoral heterogeneity in the brain of apatient is accessed by defining two separate ROIs within an image or atime-series of images of peritumoral edema of a GBM patient. Forexample, FIGS. 2A and 2B each disclose an MR image of a patient having aGBM 10 with an enhancing part 12 located within an area of peritumoraledema 14. A first ROI 16 is shown in FIG. 2A (which is a post-contrastT1-weighted MR image) and is located in the image immediately adjacentto the enhancing part 12 of the tumor 10 within the peritumoral edema14. A second ROI 18 is shown in FIG. 2B (which is a T2-FLAIR MR image)and is located at a farthest distance from the enhancing part 12 of thetumor 10 but still within the peritumoral edema 14 along a peripherythereof.

After the two regions of interest, 16 and 18, are selected and defined,Principal Component Analysis (PCA) is employed to summarize theperfusion temporal dynamics of each of the two ROIs, 16 and 18, into agroup of few principal components that may capture, for instance, morethan about 95% of the signal's variance. Thereafter, the Bhattacharyyacoefficient, which provides a measure of the amount of overlap betweentwo statistical samples or populations, is used to measure theseparability between these summarized perfusion measurements of the twoROIs, 16 and 18 for an individual patient, thereby providing a biomarkerof the EGFRvIII expression. This separability measurement is hereinafterreferred to as a Peritumoral Heterogeneity Index (PHI), or φ-index. SeeFIG. 1.

By way of example, a value of φ-index which may be close to zero (0)indicates similar perfusion dynamics between the two ROIs, 16 and 18,which is consistent with a deeply and aggressively infiltrating tumor.On the other hand, a value of φ-index which may be closer to one (1)indicates substantial difference between the perfusion of the two ROIs,16 and 18, which is consistent with less infiltrative tumor phenotypeswhose tumor-like perfusion characteristics are relatively confined tothe vicinity of the bulk tumor.

FIG. 1 provides an example showing the results of distributions ofseparability measurements (i.e., a Peritumoral Heterogeneity Index)between the two ROIs, as discussed above, using the Bhattacharyyacoefficient across a patient population of each mutation status (i.e.,negative and positive).

For purposes of example, the φ-index was initially estimated for adiscovery cohort of 64 subjects (42 EGFRvIII-negative) diagnosed with denovo GBM and found to display significantly distinct distributionsbetween EGFRvIII-negative and EGFRvIII-positive patients, with median φvalues of 0.3766 and 0.08, respectively (p=3.0531×10⁻¹⁰ AUC=0.9697).Subsequently, an independent replication cohort of 78 subjects (58EGFRvIII-negative) was analyzed in the same manner, and thedistributions of the (φ-index for the EGFRvIII-negative andEGFRvIII-positive tumors returned equivalently distinctive results, withmedian values of 0.2738 and 0.0854, respectively (p=1.2973×10⁻⁵,AUC=0.8827).

Furthermore, the discovery and replication cohorts discussed above werecombined into one large cohort of 142 subjects (100 EGFRvIII-negative),and the distributions of the φ-index for EGFRvIII-negative andEGFRvIII-positive tumors returned median values of 0.3139 and 0.0854,respectively (p=2.4668×10⁻¹³, AUC=0.9176). See FIG. 1 for the φ-index 20of EGFRvIII-negative patients and the φ-index 22 of EGFRvIII-positivepatients. The results display very distinctive distributions betweenEGFRvIII-negative and EGFRvIII-positive patients. Comparison of themedian value, as well as the first and the third quartiles, between thetwo distributions reveals the ability to distinguish between them basedsolely on these measurements, while indicating the more infiltrativenature of the EGFRvIII-positive tumors. In addition, the relative extentof the infiltrative nature of the EGFRvIII-positive tumors can be shownas any particular value more closely approaches zero (0).

Use of both the two independent cohorts (for the purposes ofidentification and confirmation of the proposed φ-index) and thecross-validation over the combined cohort was essential forquantitatively validating the generalization performance of the indexand its threshold, as well as providing unbiased performance estimates.Specifically, after obtaining the φ-index in the discovery cohort (64subjects), an independent replication cohort (78 subjects) wasconstructed to confirm the generalization of its discriminatory abilityin unseen data. The two cohorts may be referred to as retrospective andprospective, since the images of the replication (i.e., prospective)cohort were obtained after the index was identified in the discovery(i.e., retrospective) cohort and the status of the mutant for allsubjects of the replication cohort was obtained after the φ-index wasestimated for all its subjects.

Once the replication cohort confirmed the generalizability of theproposed φ-index, the two cohorts were combined to a larger singlecohort (142 subjects), in order to identify the optimal threshold andevaluate its classification accuracy for the provided data using anested 10-fold cross-validation over the combined cohort using a modelconfiguration of three sets: the training set, for deriving thepredictive model; the validation set, for selecting the optimalthreshold for the φ-index; and the test set, for testing thegeneralization of predictions on new/unseen data, thereby avoidingoptimistically biased estimates of performance. During suchconfiguration, the classification accuracy was firstly estimated forvarious values of the proposed index and for each fold over the subjectsof that training set. Once the index value was found that maximizes theaccuracy in the training set, the same value was used to compute theaccuracy for the validation examples, not seen in the training set. Alarge accuracy score obtained for the training set, does not necessarilymean that the φ value used for this fold is the optimal one, as it mighthave been obtained through “overfitting” to the training data. Theaccuracy score obtained for the training set is likely to be higher thanthe more general accuracy score (actual generalization score) obtainedby applying the method with the proposed φ value to new examples, notseen in the training set. Thus, the reported cross-validated performancescore and its corresponding index threshold may be considered unbiased.The cross-validated performance was estimated equal to 89.77%, and theoptimal threshold of the φ-index was found to be 0.1377.

Variations detected in the perfusion signal between the near and farROIs of the peritumoral region, relate to phenotypic characteristicsconferred by the presence of the EGFRvIII mutation. Based on theobtained results, we note the EGFRvIII+ tumors having more densely anddeeply infiltrating fronts throughout the edematous tissue (i.e., valuescloser to 0) compared to the less aggressive EGFRvIII− tumors (i.e.,values closer to 1). This finding likely relates to theneovascularization associated with EGFRvIII+ tumors, which may bedetected by DSC-MRI. This property enabled us to derive a sensitive andspecific imaging biomarker based on DSC-MRI. Specifically, as shown inFIG. 1, the distribution of the φ-index values across the EGFRvIII−patient population has a much larger range of values [0.0325-0.8764] andinterquartile range (IQR) [0.2186-0.5008] when compared to thedistribution across the EGFRvIII+ patients (range: [0.0067-0.2466], IQR:[0.0482-0.1310]). This discrepancy might reflect underlyingheterogeneity in molecular expression, which is known to be prevalent inGBM, with the EGFRvIII− patients potentially expressing the mutant inareas that were not sampled for tissue analysis, and tumors that werefound to be EGFRvIII+ being more likely to have developed the fullphenotype of the mutant.

Furthermore, the narrow range of the φ-index distribution across theEGFRvIII+ patients means that high specificity, in terms of identifyinga new/unseen EGFRvIII+ patient, can be achieved without significant lossof sensitivity. This is important for personalized selection of EGFRvIIItherapies during the course of a treatment, e.g., if the mutant existsor develops post-operatively, the disclosed approach would detect andmonitor its status across a regional volume and allow for potentialmodification of the applied treatment.

For purposes of statistically evaluating the significance of theobtained results, a two-tailed paired t-test was used between the twodistributions in the above referenced combined cohort of 142 subjects.This statistical analysis returned a p-value=2.4668×10⁻¹³, whichconfirmed at the 5% significance level that the subjects in the pool ofEGFRvIII− and EGFRvIII+, come from populations with unequal means, withthe confidence interval on the difference of the means being [0.1921,0.3162].

A receiver operating characteristic (ROC) analysis was also used in thecombined cohort to illustrate the performance of the proposed approachon an individual patient basis (see FIG. 7). The ROC curve was createdby plotting the sensitivity against the false positive rate (i.e.,1-specificity) at various threshold settings of the φ-index. Thethreshold set on 0.1377 returned the best accuracy for the proposedapproach in the combined cohort, namely equal to 88.73%. The area underthe ROC curve is 0.9176, with standard error equal to 0.0304, and 95%confidence interval in the range of 0.8579-0.9773.

Although calculation of the φ-index may be mostly automated, such as viause of computer software or the like, expert drawing and defining ofnear and far ROIs may be required according to some embodiments. To testthe robustness and reproducibility of the index with respect to thisexpert input, the intra- and inter-rater agreement was evaluated usingthe Intra-class Correlation Coefficient (ICC). Specifically, 40 subjectsof the combined cohort were randomly selected and new near/far regionsof interest were annotated by: a) the same operator but on a differentinstance (3 months later); and b) another independent operator. Therepeated set of ROIs was drawn in a much faster and less detailed way,in order to test the reproducibility of the φ index in a more typicalclinical setting. The median φ index values for the intra-rater subsetwere 0.2761 and 0.065 for EGFRvIII− and EGFRvIII+ subjects, respectively(p=2.8529×10⁻⁵, AUC=0.8846), whereas the median φ values for theinter-rater subset were 0.2273 and 0.1112 (p=0.003, AUC=0.8242). The ICCwas computed as 0.825 and 0.725 for the intra- and the inter-rateragreement, respectively.

Accordingly, embodiments disclosed herein provide robust, reproducible,non-invasive and clinically relatively easy-to-perform processes toevaluate the imaging signature of the EGFRvIII expression in GBM, orother tumors. Assessment of the heterogeneity of perfusion temporaldynamics throughout the peritumoral edematous tissue on in vivo MRI datareveals an accurate and reproducible imaging biomarker of the EGFRvIIImutation status, which is important for personalized treatment decisionsand response evaluation in patients, for instance, diagnosed with denovo GBM. The results achieved with the embodiments strongly support thestrengths of an approach referenced herein as “computational molecularimaging” which detects molecular targets by virtue of their imagingphenotypical patterns, without the need to deliver specialized molecularprobes to the tissue. Importantly, these results may be obtained usingcommonly acquired clinical MRI scans in a clinic.

The ability to non-invasively determine the mutation status of EGFRvIIIin patients with GBM, only by assessing MRI perfusion scans, can assistin obtaining the oncogene status quicker and more easily. Application ofPCA in the raw DSC-MRI signal reveals informative features thatrepresent distinctive imaging phenotypes correlating to the EGFRvIIIstatus in GBM. The obtained results suggest that the discrimination ofthe EGFRvIII mutation status, which is critical for personalizedtreatment decisions and response evaluation, can be achieved basedsolely on assessing the peritumoral heterogeneity on in vivo perfusionimaging data, whilst obviating costly and not widely-availabletissue-based genetic testing. The proposed φ-index contributes toprecision medicine, by allowing the identification of an importantmolecular target on an individual patient basis, using widely availableclinical imaging protocols, hence enabling the possibility of treatmentstargeted to the needs of each individual faster and more easily than thecurrently invasive options, with the intention of improving patientprospects while minimizing the risk of side effects.

In still another embodiment, the imaging system and apparatus providedherein allows for non-invasive diagnosis of patients having EGFRmutation-associated cancers. This information may be used for treatmentof these cancer patients. In certain embodiments, the imaging providedby the system and apparatus described herein allows for targetedsurgical resection of operable EGFR associated tumors. For example, theembodiments in the examples herein provide a global estimate of EGFRoverexpression and/or mutant expression, which may be used for anaggressive surgical resection (if positive for EGFR mutant). In certainembodiments, this index might capture EGFR mutant expressioncharacteristics even if the histopathologically-analyzed tissue segmentturns out to be negative. Thus, this method may be the primary source ofinformation about EGFR overexpression and/or mutant expression, since itwould mitigate the usual tissue sampling limitation. This method can beused for screening people into treatment trials prior to surgery. Inaddition to these, because of the way in which this index isconstructed, it is anticipated that the method can provide a spatial mapof EGFR overexpression and/or mutant expression, rather than a globalmeasure, and this can guide more targeted surgical resection andradiation (e.g. with protons).

The methods and systems described herein are useful in monitoring theprogress of a patient following initiation of treatment. The systemallows detection of one or more of: reduced tumor size, reducedperitumoral edematous tissue, and/or reduced presence of tissue havingan EGFR mutation. Such patients may have cancers such as glioblastoma,or another EGFR mutation-positive cancer such as lung cancer, includingnon-small cell lung cancer, among others.

In certain embodiments, the imaging system and methods provided hereinmay be combined with targeted resection and/or other treatmentsconsistent with the current standard of care, e.g., radiation,chemotherapy, EGFR mutation-targeted treatments, or other non-targetedtherapy. Examples of suitable targeting therapies for EGFR, include,e.g., Celldex′ CDX-110 (rindopeptimut) with GM-CSF; PF299804, a Pan-HERIrreversible Inhibitor (Pfizer/Grupo Español de Investigación enNeurooncología); AMG 595 (Amgen); temozolomide (ABT-414, commerciallyavailable as TEMOMAR®, (temozolomide), Abbott; Erlotinib (Tarceva®,OSI-774) (Genetech); HM781-36B in HNSCC (Yonsei University), amongstothers. Still other targeted therapies may include the chimeric antigenreceptor (CAR) T-cell therapies, e.g., (trials NCT01454596 andNCT02209376); S. Gil and CH June, Immunol Rev, 2015 January; 263(10:68-89; S. Krebs et al, Front Oncol, 2013; 3:322 (2013 Dec. 31); M Kaloset al, Science Translation Medicine, Vol. 3, Issue 95, pp 95ra73 (10Aug. 2011). Suitable doses for the patient may be readily determined.For example, temozolomide 75 mg/m2 for 42 days concomitant with focalradiotherapy followed by initial maintenance dose of 150 mg/m2 oncedaily for Days 1-5 of a 28-day cycle of temozolomide for 6 cycles.Alternatively, it may be delivered at an initial dose of 150 mg/m2 oncedaily for 5 consecutive days per 28-day treatment cycle. In anotherembodiment, temozolomide as an infusion is recommended to be deliveredas an intravenous infusion over 90 minutes is the same as the dose forthe oral capsule formulation. In another embodiment, erlotinib may bedelivered orally, at a dose of about 25 mg, 100 mg, or 150 mg orally,once daily. Still other suitable doses and regimens may be determined.Non-targeted therapies, e.g., carmustine (BCNU) and cis-plastinum(cisplatin) are known to those of skill in the art and are not alimitation of the present invention.

The process of the embodiments disclosed herein may be used andevaluated in recurrent, as well as in post-treatment tumors, in order toconfirm assessment of their mutant expression levels. Furthermore, alarger cohort may be utilized for a multivariate analysis, including theexpression levels of the wt-EGFR amplification, as well as of the otherEGFR mutations.

Since the mutant can be sparsely expressed throughout the extent of thetumor, retrieving the mutant status on specific spatially distinctradiologically-guided localized biopsies may be useful. Thus, accordingto one embodiment, the proposed φ-index may be employed for evaluatingthe mutant on these specific known locations. Also, a larger cohort maybe considered for analysis, consisting of subjects scanned usingdifferent equipment, to validate the robustness of the proposed markerto acquisition differences.

According to one embodiment, the above referenced process and techniquesmay be provided in the form of computer software that, when executed onan electronic processor, is able to automatically classify healthy andunhealthy tissue based on data generated from imaging signals, such asdynamic susceptibility contrast MRI (DSC-MRI). For instance, thecomputer software may be used to analyze MRI data in a unique mannerenabling accurate identification of tumor infiltrated tissue, such as inGBMs. Thus, the software may be used to provide an accurate predictionof cancer localization, infiltration, and recurrence, in particular forGBMs.

The software, provided with the raw DSC-MRI data, may apply an advancedmulti-variance statistical procedure, such as PCA, which constructssubtle analyses of tumor heterogeneity. In particular, PCA may beapplied to a pair of ROIs, a first ROI on the edematous tissueimmediately adjacent to the enhancing part of the tumor and a second ROIat a farthest spaced location from the tumor but still within theedematous tissue.

Thereafter, the Bhattacharyya coefficient, which provides a measure ofthe amount of overlap between two statistical samples or populations,may be applied by the software to measure the separability between thesesummarized perfusion measurements of the two regions of interest,thereby providing a biomarker, for instance, of the EGFRvIII expression.Accordingly, use of the above referenced software provides the abilityto visualize the peritumoral region with a high degree of precision inGBM patients and permits subtle differences to be recognized.

A further embodiment may also include at least one non-transitorycomputer readable storage medium having computer program instructionsstored thereon that, when executed by at least one processor, can causethe at least one processor to perform any of the process steps describedabove.

According to another embodiment, a system is provided for carrying outthe above reference method. The system may include software or the likeprovided on a circuit board or within another electronic device and caninclude various processors, microprocessors, controllers, chips, drives,MRI or other imaging machines, and the like. It will be apparent to oneof ordinary skill in the art that systems, modules, processors, servers,and the like may be implemented as electronic components, software,hardware or a combination of hardware and software for purposes ofproviding the system.

Example

A specific example of use of the above disclosed method is providedbelow. This method is not a limitation on the invention.

Study Design:

The data used for this example comprises preoperative multi-parametricMRI data from a retrospective cohort of 142 patients (80 males, 62females) with de novo GBM. The inclusion criteria for these patientscomprised; a) the diagnosis of de novo GBM based on pathology, b)EGFRvIII mutation status based on next generation sequencing, and c)availability of contrast-enhanced T1-weighted, T2-FLAIR, and DSC MRIimage volumes. Tissue specimens of these patients were obtained bysurgical resection and tested for the status of the EGFRvIII mutation.The patient's mean and median age in years was 59.82 and 60.95,respectively (range: 18.65-86.95). The population of 142 patients wasdistinguished in 100 (70.42%) with EGFRvIII-negative (57 males, 43females) and 42 (29.58%) with EGFRvIII-positive (23 males, 19 females)status, which is consistent with the incidence of the mutation asmentioned in literature, namely 24-67% [A. B. Heimberger, et al. Thenatural history of EGFR and EGFRvIII in glioblastoma patients. Journalof Translational Medicine 3, (2005)].)]. In the studies describedherein, gender and age did not differ significantly between theEGFRvIII− and the EGFRvIII+ patients. No randomization method was usedfor allocating samples to experimental groups.

Equipment and Imaging Data:

The histological confirmation of GBM diagnosis was performed by a BoardCertified neuropathologist reviewing the pathology of surgicallyresected tissue, according to the WHO classification criteria. The mostrepresentative block per resected tissue specimen was chosen by theneuropathologist on the basis of morphology and was included for geneticanalysis. The advantage of this and the ability to use Formalin-fixedParaffin-embedded (FFPE) tissue lies upon the knowledge of the precisecharacteristics of the material used for RNA extraction, as opposed toother assays based on fresh tissue, in which one may be testing necrosisor inflammation instead of the highest number of tumor cells possible,without the ability to quality control what goes into the assay. Anin-house NGS-based assay to detect EGFRvIII transcripts (25, 26) hasbeen developed, which was validated with detection by Taqman ReverseTranscription-Polymerase Chain Reaction (RT-PCR). Total nucleic acid wasextracted from FFPE tissue, and complementary DNA was then synthesizedfrom RNA. PCR primers were designed to capture EGFR wild-type, EGFRvIII,three housekeeping genes, and three primer sets with increasing targetsizes to assess the level of RNA degradation in the sample. Thesequencing library preparation method was a two-step PCR, with multiplexPCR followed by a second PCR to add Illumina sequencing index andadaptors. Subsequently, the sequencing library was quantified, sequencedon Illumina MiSeq, and analyzed using a bioinformatics pipelinedeveloped in our lab, “EGFRvIII Picker”. EGFRvIII ratio was calculatedby the following formula: EGFRvIII reads/(EGFRvIII reads+ EGFR wild-typereads). Based on our results using normal brains and GBMs, our cut-offfor EGFRvIII+ is >30% EGFRvIII to wild-type allele ratio.

Determination of EGFRvIII Mutation Status:

The histological confirmation of all tumors was performed by anexperienced neuropathologist reviewing the pathology of surgicallyresected tissue, according to the WHO classification criteria. The mostrepresentative block per resected tissue specimen was chosen by theneuropathologist on the basis of morphology and was included for geneticanalysis. The advantage of this and the ability to use Formalin-fixedParaffin-embedded (FFPE) tissue lies upon the knowledge of the precisecharacteristics of the material used for RNA extraction, as opposed toother assays based on fresh tissue, in which one may be testing necrosisor inflammation instead of the highest number of tumor cells possible,without the ability to quality control what goes into the assay. assay.An in-house NGS-based assay to detect EGFRvIII transcripts (25, 26) hasbeen developed, which was validated with detection by Taqman ReverseTranscription-Polymerase Chain Reaction (RT-PCR). Total nucleic acid wasextracted from FFPE tissue, and complementary DNA was then synthesizedfrom RNA. PCR primers were designed to capture EGFR wild-type, EGFRvIII,three housekeeping genes, and three primer sets with increasing targetsizes to assess the level of RNA degradation in the sample. Thesequencing library preparation method was a two-step PCR, with multiplexPCR followed by a second PCR to add Illumina sequencing index andadaptors. Subsequently, the sequencing library was quantified, sequencedon Illumina MiSeq, and analyzed using a bioinformatics pipelinedeveloped in our lab, “EGFRvIII Picker”. EGFRvIII ratio was calculatedby the following formula: EGFRvIII reads/(EGFRvIII reads+ EGFR wild-typereads). Based on our results using normal brains and GBMs, our cut-offfor EGFRvIII+ is >30% EGFRvIII to wild-type allele ratio.

Image Preprocessing

The provided MRI volumes were smoothed using a low-level imageprocessing method, namely Smallest Univalue Segment AssimilatingNucleus, in order to reduce high frequency intensity variations (i.e.,noise) in regions of uniform intensity profile while preserving theunderlying structure (32). The intensity non-uniformities caused by theinhomogeneity of the magnetic field during image acquisition wereremoved using a non-parametric, non-uniform intensity normalizationalgorithm (33). The volumes of all the modalities for each patient wereco-registered to the T1-CE anatomic template using a6-degrees-of-freedom affine registration, and then skullstripped (34).

Regions of Interest and Perfusion Temporal Dynamics

To assess the tumor cell infiltration heterogeneity within theperitumoral edema (i.e., the peritumoral area described byhyper-intensity on the T2-FLAIR volumes) (30), two regions of interest(ROIs) were annotated for each patient by an expert (FIG. 1), blinded toEGFRvIII status. These two ROIs were used to sample tissue located onthe two boundaries of edema: near to and far from the tumor,respectively, and hence to evaluate the heterogeneity or spatialgradient of perfusion signals. The T1-CE and T2-FLAIR volumes were usedto define the ROIs near to and far from the tumor, respectively.Specifically, the T1-CE volume was used to initially define the ROIadjacent to the enhancing part of the tumor, described by hyper-intensesignal on T1-CE, and the T2-FLAIR volume was then used to revise thisROI in terms of all its voxels being within the peritumoral edematoustissue, described by hyper-intense signal on the T2-FLAIR volume. TheT2-FLAIR volume was also used to define the ROI at the farthest from thetumor but still within the edematous tissue, i.e. the enhancing FLAIRabnormality signal. These ROIs are described by lines drawn in multipleslices of each image (T1-CE and T2-FLAIR) for each subject, whereas thevisual example of FIGS. 2A and 2B show only a single slice. Theperfusion temporal dynamics for each of these ROIs were obtained fromthe DSC-MRI volume (FIG. 2.a). Specifically, the perfusion of each voxelduring 45 time-points was used to form a feature vector of 45dimensions. Principal component analysis (PCA) was then used tosummarize the perfusion signal of each ROI, as in (31).

Specifically, the property of PCA to represent data as an ellipsoidalpopulation in a lower dimensional space, whilst retaining most of itsvariance, was exploited on these feature vectors. As shown in FIG. 3B,each of these feature vectors can be represented as a single point in a3-dimensional space. The voxels of each ROI, with similar dynamicbehavior, would form almost elliptical clusters of points (ellipsoids)in this 3-dimensional space. It should be noted that while drawing theseROIs, 1) the voxels of both ROIs are always within the edema, 2) not inproximity to the ventricles, 3) representative of infiltration intowhite matter and not into grey matter, 4) the distant ROI is at thefarthest possible distance from the enhancing part of the tumor whilestill within edema, and 5) no vessels are involved within any of thedefined ROIs, as denoted in the T1-CE volume.

Measurement of Heterogeneity

The Bhattacharyya coefficient (35) is used as a measure of heterogeneitywithin the peritumoral edema (Peritumoral Heterogeneity Index—PHI, orφ-index), by measuring the separability (range [0,1]) between thesummarized ellipsoids of these ROIs for each patient. The Bhattacharyyadistance is considered the most reliable for the purpose of measuringthe distance of a point observation from a distribution of observationsalong each principal axis, namely after conducting PCA (36). RobustnessAnalysis

Although calculation of the φ-index is mostly automated, it currentlyrequires expert drawing of the immediate and distant peritumoral ROIs(FIG. 1). To test the robustness and reproducibility of the index withrespect to this expert input, the intra- and inter-rater agreements wereevaluated, using the Intra-class Correlation Coefficient (ICC).Specifically, 40 patients of the combined cohort were randomly selectedand new ROIs were defined by a) the same operator but on a differentinstance (3 months later); b) another operator. The new set of ROIs wasdrawn in a much faster and less detailed manner, in order to test thereproducibility of PHI in a more typical clinical setting. The codesource and executable installers are available on:www.med.upenn.edu/sbia/-phiestimator.html.

Results Brief Description of the Experiment

To assess the peritumoral heterogeneity, two ROIs were defined withinthe peritumoral edema: one ROI adjacent to the enhancing part of thetumor, typically depicted by high T1-CE signal intensity, and the otherat the farthest from the tumor but still within the edema FIGS. 2A and2B. The average size for the near and far ROIs (across all subjects) is72.2 and 172.3 voxels, respectively, which can be considered sufficientin order to account for potentially noisy voxels included in the ROIs. Arecently published method (31) utilizing PCA was employed to summarizethe perfusion temporal dynamics of each ROI into a group of fewprincipal components capturing more than 95% of the signal's variance.The Bhattacharyya coefficient (35) was then used to measure theseparability between the summarized perfusion measurements of the twoROIs for each patient, and evaluated as a biomarker of EGFRvIII. Werefer to this separability measurement as Peritumoral HeterogeneityIndex (PHI), or φ-index. Values of the φ-index close to 0 indicatesimilar perfusion dynamics between the two ROIs, consistent with deeplyand aggressively infiltrating and vascularized tumors (37). Such valuescould also indicate normal phenotype throughout the edema, which israther unlikely in these aggressively infiltrating tumors. Conversely,values of the φ-index close to 1 indicate substantially differentperfusion characteristics between the two ROIs, consistent with a lessmigratory phenotype of more localized peritumoral infiltration andvascularization, in which tumor-like perfusion characteristics arerelatively confined to the vicinity of the tumor. PHI yieldssignificantly distinct distributions for EGFRvIII+ and EGFRvIII−patients

The φ-index was initially estimated for a discovery cohort of 64patients (22 EGFRvIII+) with de novo GBM and displayed significantlydistinct distributions between EGFRvIII− and EGFRvIII+ patients(p=1.5725×10-7, AUC=0.9459), with median φ values of 0.3097 and 0.0961and Inter-Quartile Range (IQR) of [0.1855-0.4808] and [0.0509-0.1095],respectively (FIG. 4A). Subsequently, an independent replication cohortof 78 patients (20 EGFRvIII+) was analyzed in the same way, and theφ-index distributions for the EGFRvIII- and EGFRvIII+ tumors returnedequivalently distinct results (p=2.8164×10-4, AUC=0.8336), with medianvalues of 0.2586 (IQR: [0.1659-0.3938]) and 0.0952 (IQR:[0.0411-0.1348]), respectively (FIG. 4B). The best thresholdaccuracy=0.9219, specificity=0.9762, sensitivity=0.8182) in the φ-indexfor the discovery cohort was 0.1372, which when applied to thereplication cohort returned an accuracy of 0.8590 (specificity=0.8793,sensitivity=0.8) confirming its generalizability.

Furthermore, the two cohorts were combined into one larger cohort, of142 patients (42 EGFRvIII+), and the distinctiveness of thedistributions of the φ-index for EGFRvIII- and EGFRvIII+ tumors was evenmore significant (p=4.0033×10-10, AUC=0.8869), with median values of0.2806 (IQR: [0.1759-0.4088] and 0.0961 (IQR: [0.0505-0.1309]),respectively. Comparison of the median values, as well as the first andthe third quartiles, between the two distributions reveals the abilityto distinguish between them based solely on the PHI (FIG. 4A). Tostatistically evaluate the significance of the results obtained for thecombined cohort, a two-tailed paired t-test was used to compare betweenthe two distributions (FIG. 3C). This statistical analysis returned apvalue=4.0033×10-10, which confirmed at the 5% significance level thatthe patients in the pool of EGFRvIII- and EGFRvIII+, come frompopulations with unequal means, with the confidence interval (CI) on thedifference of the means being [0.1526, 0.2795]. A receiver operatingcharacteristic (ROC) analysis was also used in the combined cohort toillustrate the performance of PHI on an individual patient basis (FIG.5I). The ROC curve was created by plotting the sensitivity against thefalse positive rate (i.e., 1-specificity) at various thresholds of PHI.The threshold set on 0.1377 returned the best accuracy (88.73%), withsensitivity and specificity equal to 80.95% and 92%, respectively(AUC=0.8869, standard error 0.0351, 95% CI: [0.8180-0.9558]).

To demonstrate that the results of perfusion heterogeneity between thetwo ROIs for each subject within the two EGFRvIII groups were notconfounded by a potentially larger extent of edema in one of the twogroups, we assessed the distance between the two ROIs against theφ-index for each patient (FIG. 8) and noted that there is no correlationbetween them (correlation coefficient: 0.0519, p-value=0.5394).Furthermore, by assessing the distribution of distances between the twoROIs for each EGFRvIII group, we show that the extent of the edemabetween these two groups has no significant difference (p-value=0.6728)(FIG. 9). This shows that the obtained results of PHI variation betweenEGFRvIII+ and EGFRvIII− tumors is a true finding and not an effect ofdifferent amount of edema observed between the two groups.

Unbiased Estimates of Performance Through Nested Cross-Validation

A nested 10-fold cross-validation was also performed over the combinedcohort using a model configuration of three sets: the training set, forderiving the predictive model; the validation set, for selecting theoptimal threshold for the φ-index; and the test set, for testing thegeneralization of predictions on new/unseen data, thereby avoidingoptimistically biased estimates of performance. The cross-validatedaccuracy, sensitivity and specificity were estimated equal to 89.92%,83.77% and 92.35%, respectively, and the optimal threshold of theφ-index was found to be 0.1377 in consistency with the one found in theROC analysis.

Repeatability and Reproducibility of PHI

The median φ values for the intra-rater subset were 0.2761 (IQR:[0.1572-0.4046]) and 0.065 (IQR: [0.0389-0.1303]) for EGFRvIII- andEGFRvIII+ patients, respectively (p=2.8529×10-5, AUC=0.8846), whereasthe median φ values for the inter-rater subset were 0.2273 (IQR:[0.1512-0.3426]) and 0.1112 (IQR: [0.0579-0.1294]) (p=0.003,AUC=0.8242). The ICC was 0.825 among the same rater and 0.775 amongdifferent raters.

Discriminative Value of Other MRI Modalities

Additional MRI modalities were assessed to investigate theirdiscriminative ability, compared to that of the DSC-MRI. Thesecomprised: native T1-weighted (T1), T1-CE, T2, T2-FLAIR, DTI(TR),DTI(FA), DTI(RAD) and DTI(AX). The number of patients was reduced to 140due to data availability. It is observed that all additional modalitieshad notably poorer discrimination ability (FIGS. A-4H) and thedistributions of PHI for each of them were not distinct between thedifferent EGFRvIII genotypes. Furthermore, a support-vector-machine wasused for a multivariate analysis of a complete joint/multifaceted model,where only the DTI(TR) (p=0.0053) and T1 (p=0.0054) were found to besignificant (38), additive to the DSC. ROC analysis of these two jointmodels, namely DSC-TR and DSC-TR-T1 showed very small improvement overDSC-MRI alone (FIG. 5I, FIG. 5K), with AUCDSC=0.8857, AUCDSC-TR=0.8985and AUCDSC-TR-T1=0.9019.

Discussion

This study is the first to establish a robust, reproducible,non-invasive and easy to evaluate imaging signature of EGFRvIII in denovo GBM, based on quantitative analysis of peritumoral regions and noton assessment of intratumoral regions that the current general knowledgeand understanding of the EGFRvIII status in GBMs is currently based. Theresults demonstrate that assessment of the heterogeneity of perfusiontemporal dynamics throughout the peritumoral edema on in vivo MRI datapredicts the EGFRvIII mutation status, hence reveals an accurate(89.92%), sensitive (83.77%) and specific (92.35%) imaging biomarker ofthe mutation, which can be used clinically for personalized treatmentdecisions and response evaluations.

EGFRvIII identification in GBM patients using radiographic analysisalone holds significant clinical relevance in terms of personalizedmedicine. Traditional identification of genomic mutations, such asEGFRvIII by tissue-based techniques, requires invasive surgicalresection or biopsy, and is obtained from a single tissue specimen,whereas we report a non-invasive, purely image-based approach forpre-operative evaluation of this molecular target. Since glioma cellsbearing the mutant are not uniformly distributed throughout a tumor,sampling error may occur with tissue-based approaches. Conversely,imaging captures the tumor's spatial heterogeneity more completely,minimizes bias potentially occurring by evaluating a limited portion oftumor, and can provide data on the regional EGFRvIII expression. Suchglobal assessment of the mutant could be used as a more accurate guideto patient selection for clinical trials. Furthermore, once the mutationis identified, EGFRvIII-targeted therapies can be selected. In additionto selecting initial treatment, there may be significant value indetection of EGFRvIII at additional time-points following treatmentinitiation, as it has been shown that expression of the mutant may belost at the time of progression (29) following standard chemo-radiationin approximately half of patients (28). There is also a high probability(>80%) of losing EGFRvIII expression following EGFRvIII peptidevaccination (27). Consistent with this finding, antigen editing withquantitative loss of EGFRvIII is also observed, after infusion ofgenetically modified chimeric antigen receptor (CAR) T-cells targetingEGFRvIII in recurrent GBM patients (21). By using standard clinicalimaging sequences, a longitudinal evaluation of EGFRvIII in patientsboth after treatment and with recurrent tumors, represents a feasibleapproach to detect changes in EGFRvIII expression. Unlike repeatedbiopsies, such monitoring can be performed repeatedly without risk andwith decreased cost over time. Thus, an imaging-based approach forEGFRvIII identification can aid in all phases of care of the GBM patientfrom diagnosis to targeted therapy to response surveillance. Althoughmost of the attention in characterizing tumors has been placed on thetumor bulk, the peritumoral edema, typically depicted by high T2-FLAIRsignal intensity, holds much additional data. Despite the fact that morethan 90% of recurrences occur in edema (39) due to the highlyinfiltrative nature of GBM, there is limited research focused on theassessment of this region and its microenvironment (2, 40). Edemaresults from infiltrating tumor cells and the biological response to theangiogenic and vascular permeability factors released by the spatiallyadjacent tumor cells (31). Although the peritumoral edema remains mostlyunresected and is generally not aggressively treated, by virtue ofhosting the tumor's “propagating font” it is critically important fordiagnostic and therapeutic purposes.

Large GBM tumors typically outgrow their blood supply, which results inischemia, secretion of angiogenic factors, such as vascular endothelialgrowth factor (VEGF), and cytokines that eventually lead toneovascularization, increased permeability, and edema (41, 42). Thesenew vessels, when compared with the existing healthy blood vessels, havean increasingly tortuous and branched structure, as well as higherpermeability, which typically affect the brain circulation. Suchalterations in the brain circulation are captured by DSC-MRI, which isbased on the decay of T2 signal during the first pass of a paramagneticcontrast medium through the capillary bed. Therefore, DSC-MRI enablesthe generation of a perfusion curve by assessing the dynamic changes inthe signal intensity of the peritumoral region of a GBM through time.Analysis of the complete perfusion signal through PCA enablesmicrovascular imaging and provides a visual correlation of blood flow,blood volume, and vessel permeability (31).

Variations in the perfusion signal between the immediate and distantperitumoral ROIs relate to phenotypic characteristics conferred by thepresence of EGFRvIII. Based on the φ-index, we found that EGFRvIII+tumors had very similar, and relatively normal immediate and distantperitumoral perfusion patterns, in contrast to the EGFRvIII-tumors (FIG.3). This finding is consistent with relatively more locally infiltratingEGFRvIII− tumors, accompanied by localized immediate peritumoralvascularization. Note that “more locally infiltrating” does not refer toa generally more infiltrative tumor. Conversely, deeply infiltrating andmigrating EGFRvIII+ tumors displayed a more uniform peritumoralperfusion phenotype, consistent with less intense peritumoralvascularization facilitated by the migratory characteristics ofEGFRvIII+ tumors that likely allows them to gain access to blood supplyfarther from the bulk of the tumor. Differences in the perfusion signal(FIG. 3) enabled us to derive an accurate, sensitive and specificimaging biomarker based on DSC-MRI. Specifically, the distribution ofthe φ-index values (FIG. 4C) across the EGFRvIII population has a muchlarger range of values [0.0340-0.8944] and IQR [0.1759-0.4088] whencompared to the distribution across the EGFRvIII+ patients (range:[0.0080-0.5039], IQR: [0.0505-0.1309]). This discrepancy might reflectthe underlying expression heterogeneity (2-5), which is prevalent inGBM, with the EGFRvIII− patients potentially expressing the mutant formin areas that were not sampled for tissue analysis, and tumors that werefound to be EGFRvIII+ being more likely to have developed the fullphenotype of the mutant. It is well-documented that oncogenic EGFRvIIIconfers a more motile and invasive phenotype to neural stem cells (43)and GBM cells (37). Furthermore, the narrow range of the φ-indexdistribution across the EGFRvIII+ patients suggests high specificity interms of identifying a new EGFRvIII+ patient, which can be achievedwithout significant loss of sensitivity. DSC-MRI alone was the focus ofthis study, even though is an advanced imaging modality that is notalways available. However, mounting evidence for the importance of thismodality (18, 31, 40) has rapidly increased its adoption in standardclinical settings. Nevertheless, assessment of additional MRI modalitiesto investigate if a joint/multifaceted model of the peritumoralheterogeneity could lead to an improved biomarker of EGFRvIII showedthat only DTI(TR) and T1 were significant, in addition to DSC-MRI (FIG.5K-FIG. 5I). However, considering the improvement offered by includingthese modalities, we still think there is little value in adding otherMRI modalities to DSC, unless those additional sequences are acquiredanyway for other reasons. Notably, we found that the differences in PHIbetween the EGFRvIII+ and EGFRvIII− patients were minimal for DTI(TR)(FIG. 6). This would be consistent with similar cell density between thetwo defined ROIs and for both EGFRvIII genotypes. The actual differencebetween the EGFRvIII+ and the EGFRvIII− patients lays in the gradient ofvascularization throughout the edema that looks to be almost identicalfor the EGFRvIII+ patients, as opposed to the EGFRvIII− patients, whoshow a larger drop in the perfusion of the immediate peritumoral ROI,i.e. more confined infiltration and vascularization (FIG. 3, FIG. 7).This implies that the EGFRvIII− patients might benefit from a slightlymore extended resection and focused radiation, in order to include theimmediate peritumoral edema.

Our study has several aspects that distinguish it from prior relatedstudies (18, 40, 44-47), which were either demonstrating population-wideassociations, thereby not focusing on establishing an individual-patientbiomarker, or not validating their results in an independent replicationcohort, which is critical for a clinically useful biomarker. Firstly,and most importantly, the results obtained in our study are based onindividual-patient in vivo measures and show high accuracy in additionto providing pathophysiological insights, hence increasing thelikelihood of the φ-index being clinically applicable. Secondly, insteadof limiting the use of perfusion imaging in retrieving isolatedhemodynamic features (e.g., leakage corrected relative cerebral bloodvolume (FIG. 10A-FIG. 10B), we employ the complete perfusion signal viaPCA, which allows for more comprehensive analysis as it encapsulates thecomplete hemodynamic information. Thirdly, instead of reporting onlyresults on a discovery set, we use two independent cohorts for thepurpose of dentification (initial discovery set) of the proposed φ-indexand confirmation (independent replication cohort) of its discriminatorygeneralizability in unseen data. These two cohorts could be noted asretrospective and prospective, since the images of the replication(i.e., prospective) cohort were obtained after the index was identifiedin the discovery (i.e., retrospective) cohort, and EGFRvIII status forthe replication cohort was obtained after the φ-index was estimated forall its patients. Furthermore, we combined these two cohorts under anested cross-validation scheme, to quantitatively validate thegeneralization performance of PHI and its threshold, whilst providingunbiased performance estimates. The advantage of cross-validation liesupon the observation that high accuracy score obtained for the trainingset, might have been obtained through “overfitting” to the trainingdata. The accuracy score obtained for the training set is likely to behigher than the accuracy score obtained by applying the method to newexamples, not seen in the training set. Thus, the reportedcross-validated performance score and its corresponding φ-indexthreshold may be considered unbiased. Additionally, none of theseprevious studies investigate for the reproducibility of their findings,whereas in our case both inter- and intra-rater agreements are evaluatedin almost one third of the included data. This study evaluated theexpression status of EGFRvIII alone, as a binary present/absent value,and did not account for other mutations or amplifications in EGFR thatmight alter the perfusion signal. However, the frequency of EGFRvIII wassimilar to the rates reported in the literature (11). It is known thatEGFR amplification may increase between the initial diagnosis andrecurrence (28), and that loss of EGFRvIII may be due to EGFRamplification and individual cells harboring varying levels of EGFRvIII(48) or even regulated by the tumor (49). Thus, it would be informativeto include the EGFR amplification values in a future analysis, whichcould further explain the widespread of PHI values for the EGFRvIII−tumors. We currently consider patients labeled as EGFRvIII−, but withlow φ-index values, as patients that may potentially express the mutantin areas that were not sampled for tissue analysis, resulting ininappropriate classification. Future prospective studies could beconducted for retrieving the mutant status on specific spatiallydistinct radiologically-guided localized biopsies, as described in otherstudies (4, 50). Then, the proposed φ-index would be employed forevaluating the mutant on these specific known locations, facilitatingthe creation of a parametric map of EGFRvIII expression. Last but notleast, a larger cohort should be considered for analysis, consisting ofpatients scanned using different equipment, with the intention ofvalidating the robustness of the proposed marker to acquisitiondifferences. The ability to non-invasively determine the status ofEGFRvIII in GBM patients, only by assessing DSC-MRI scans, can assist inobtaining the mutant status faster and more easily. Application of PCAin the raw DSC-MRI signal reveals informative features that representdistinctive imaging phenotypes correlating to EGFRvIII in GBM. ThisEGFRvIII imaging signature is constructed in a manner that should berobust to MRI scanner variations, by virtue of evaluating within-patientheterogeneity measures, rather than relying on population-wideassociations (45-47). The obtained cross-validated results demonstratethat discrimination of the EGFRvIII status, which is critical forpersonalized treatment decisions and response evaluation, can beachieved based solely on assessing the peritumoral heterogeneity on invivo perfusion imaging data, whilst potentially obviating costly and notwidely-available tissue-based genetic testing. The cross validationscheme over the available patient data provided unbiased performanceestimates and quantitatively validated the generalization performance ofthe φ-index and its classification threshold. The φ-index contributes topersonalized medicine by allowing the identification of an importantmolecular target on an individual patient basis, using widely availableclinical imaging protocols. These characteristics enable theidentification of individual patients that could benefit from selectivetreatments in a more efficient and less invasive way than by currentoptions, with the intention of improving patient prospects whileminimizing the risk of side effects.

CITATIONS

-   Johnson D R, O'Neill B P. Glioblastoma survival in the United States    before and during the temozolomide era. Journal of Neuro-Oncology.    2011; 107:359-64.-   2. Lemee J-M, Clavreul A, Menei P. Intratumoral heterogeneity in    glioblastoma: don't forget the peritumoral brain zone.    Neuro-Oncology. 2015; 17:1322-32.-   3. Aum D J, Kim D H, Beaumont T L, Leuthardt E C, Dunn G P, Kim A H.    Molecular and cellular heterogeneity: the hallmark of glioblastoma.    Neurosurg Focus. 2014; 37:E11.-   4. Sottoriva A, Spiterib I, Piccirillo S G M, Touloumis A, Collins V    P, Marioni J C, et al. Intratumor heterogeneity in human    glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad    Sci USA. 2013; 110:4009-14.-   5. Patel A P, Tirosh I, Trombetta J J, Shalek A K, Gillespie S M,    Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral    heterogeneity in primary glioblastoma. Science. 2014; 344:1396-401.-   6. O'Rourke D, Chang S. Pilot Study of Autologous T Cells Redirected    to EGFRVIII—With a Chimeric Antigen Receptor in Patients With    EGFRvIII+ Glioblastoma (ClinicalTrials.gov Identifier: NCT02209376).    2014.-   7. Gan H, Cvrljevic A, Johns T. The epidermal growth factor receptor    variant III(EGFRvIII): where the wild things are altered. FEBS    Journal. 2013; 280:5350-70.-   8. Verhaak R, Hoadley K, Purdom E, Wang V, Qi Y, Wilkerson M, et al.    Integrated Genomic Analysis Identifies Clinically Relevant Subtypes    of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1,    EGFR, and NF1. Cancer Cell. 2010; 17:98-110.-   9. Brennan C W, Verhaak R G W, McKenna A, Campos B, Noushmehr H,    Salama S R, et al. The Somatic Genomic Landscape of Glioblastoma.    Cell. 2013; 155:462-77.-   10. Humphrey P, Wong A, Vogelstein B, Zalutsky M, Fuller G, Archer    G, et al. Anti-synthetic peptide antibody reacting at the fusion    junction of deletion-mutant epidermal growth factor receptors in    human glioblastoma. Proc Natl Acad Sci USA 1990; 87:4207-11.-   11. Heimberger A B, Suki D, Yang D, Shi W, Aldape K. The natural    history of EGFR and EGFRvIII in glioblastoma patients. Journal of    Translational Medicine. 2005; 3.-   12. Arteaga C. Epidermal growth factor receptor dependence in human    tumors: more than just expression? Oncologist. 2002; 7:31-9.-   13. Nishikawa R, Ji X, Harmon R, Lazar C, Gill G, Cavenee W, et al.    A mutant epidermal growth factor receptor common in human glioma    confers enhanced tumorigenicity. Proc Natl Acad Sci USA. 1994;    91:7727-31.-   14. Heimberger A B, Hlatky R, Suki D, Yang D, Weinberg J, Gilbert M,    et al. Prognostic effect of epidermal growth factor receptor and    EGFRvIII in glioblastoma multiforme patients. Clinical Cancer    Research. 2005; 11:1462-6.-   15. Gan H K, Kaye A H, Luwor R B. The EGFRvIII variant in    glioblastoma multiforme. Journal of Clinical Neuroscience. 2009;    16:748-54.-   16. Fan Q-W, Cheng C K, Gustafson W C, Charron E, Zipper P, Wong R    A, et al. EGFR Phosphorylates Tumor-Derived EGFRvIII Driving STAT3/5    and Progression in Glioblastoma. Cancer Cell. 2013; 24:438-49.-   17. Sampson J H, Archer G E, Mitchell D A, Heimberger A B, Bigner    D D. Tumor-specific immunotherapy targeting the EGFRvIII mutation in    patients with malignant glioma. Semin Immunol. 2008; 20:267-75.-   18. Tykocinski E S, Grant R A, Kapoor G S, Krejza J, Bohman L E,    Gocke T A, et al. Use of magnetic perfusion-weighted imaging to    determine epidermal growth factor receptor variant III expression in    glioblastoma. Neuro-Oncology. 2012; 14:613-23.-   19. Kalman B, Szep E, Garzuly F, Post D E. Epidermal growth factor    receptor as a therapeutic target in glioblastoma. Neuromolecular    medicine. 2013; 15:420-34.-   20. Veliz I, Loo Y, Castillo O, Karachaliou N, Nigro O, Rosell R.    Advances and challenges in the molecular biology and treatment of    glioblastoma—is there any hope for the future? Ann Trans Med. 2015;    3:7.-   21. O'Rourke D, Desai A, Morrissette J, Martinez-Lage M, Nasrallah    M, Brem S, et al. Pilot Study of T Cells Redirected to EGFRvIII with    a Chimaric Antigen Receptor in Patients with EGFRvIII+ Glioblastoma.    Neuro Oncol. 2015; 17:v110-v1.-   22. Celldex. Phase III Study of Rindopepimut/GM-CSF in Patients With    Newly Diagnosed Glioblastoma (ACT IV) (ClinicalTrials.gov    Identifier: NCT01480479). 2011.-   23. Celldex. A Study of Rindopepimut/GM-CSF in Patients With    Relapsed EGFRvIII-Positive Glioblastoma (ReACT) (ClinicalTrials.gov    Identifier: NCT01498328). 2011.-   24. Thomas A A, Brennan C W, DeAngelis L M, Omuro A M. Emerging    therapies for glioblastoma. JAMA Neurol. 2014; 71:1437-44.-   25. Daber R, Sukhadia S, Morrissette J J. Understanding the    limitations of next generation sequencing informatics, an approach    to clinical pipeline validation using artificial data sets. Cancer    Genet. 2013; 206:441-8.-   26. Hiemenz M C, Kadauke S, Lieberman D B, Roth D B, Zhao J, Watt C    D, et al. Building a Robust Tumor Profiling Program: Synergy between    Next-Generation Sequencing and Targeted Single-Gene Testing. PLoS    One. 2016; 11:e0152851.-   27. Gedeon P C, Choi B D, Sampson J H, Bigner D D. Rindopepimut:    anti-EGFRvIII peptide vaccine, oncolytic. Drugs Future. 2013;    38:147-55.-   28. Bent MJvd, Gao Y, Kerkhof M, Kros J M, Gorlia T, Zwieten Kv, et    al. Changes in the EGFR amplification and EGFRvIII expression    between paired primary and recurrent glioblastomas Neuro-Oncology.    2015; 17:935-41.-   29. Niclou S P. Gauging heterogeneity in primary versus recurrent    glioblastoma. Neuro-Oncology. 2015; 17:907-9.-   30. Yamahara T, Numa Y, Oishi T, Kawaguchi T, Seno T, Asai A, et al.    Morphological and flow cytometric analysis of cell infiltration in    glioblastoma: a comparison of autopsy brain and neuroimaging. Brain    Tumor Pathology. 2010; 27:81-7.-   31. Akbari H, Macyszyn L, Da X, Wolf R, Bilello M, Verma R, et al.    Pattern analysis of dynamic susceptibility contrast-enhanced M R    imaging demonstrates peritumoral tissue heterogeneity. Radiology.    2014; 273:502-10.-   32. Smith S M, Brady J M. SUSAN—a new approach to low level image    processing. International Journal of Computer Vision. 1997;    23:45-78.-   33. Sled J, Zijdenbos A, Evans A. A nonparametric method for    automatic correction of intensity nonuniformity in MRI data. IEEE    Transactions on Medical Imaging. 1998; 17:87-97.-   34. Jenkinson M, Beckmann C F, Behrens T E, Woolrich M W. FSL.    NeuroImage. 2012; 62:782-90.-   35. Bhattacharyya A. On a measure of divergence between two    statistical populations defined by their probability distributions.    Bulletin of the Calcutta Mathematical Society. 1943; 35:99-109.-   36. Kailath T. The Divergence and Bhattacharyya Distance Measures in    Signal Selection. IEEE Transactions on Communication Technology.    1967; 15:52-60.-   37. Lal A, Glazer C A, Martinson H M, Friedman H S, Archer G E,    Sampson J H, et al. Mutant epidermal growth factor receptor    up-regulates molecular effectors of tumor invasion. Cancer Research.    2002; 62:3335-9.-   38. Gaonkar B, Davatzikos C. Analytic estimation of statistical    significance maps for support vector machine based multi-variate    image analysis and classification. NeuroImage. 2013; 78:270-83.-   39. Petrecca K, Guiot M-C, Panet-Raymond V, Souhami L. Failure    pattern following complete resection plus radiotherapy and    temozolomide is at the resection margin in patients with    glioblastoma. Journal of Neurooncology. 2013; 111:19-23.-   40. Jain R, Poisson L M, Gutman D, Scarpace L, Hwang S N, Holder C    A, et al. Outcome Prediction in Patients with Glioblastoma by Using    Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing    Component of the Tumor. Radiology. 2014; 272:484-93.-   41. Kerbel R S. Tumor angiogenesis: past, present and the near    future. Carcinogenesis. 2000; 21:505-15.-   42. Bullitt E, Zeng D, Gerig G, Aylward S, Joshi S, Smith J K, et    al. Vessel tortuosity and brain tumor malignancy: a blinded study.    Acad Radiol. 2005; 12:1232-40.-   43. Boockvar J A, Kapitonov D, Kapoor G, Schouten J, Counelis G J,    Bogler O, et al. Constitutive EGFR signaling confers a motile    phenotype to neural stem cells. Mol Cell Neurosci. 2003; 24:1116-30.-   44. Arevalo-Perez J, Thomas A A, Kaley T, Lyo J, Peck K K, Holodny A    I, et al. T1-Weighted Dynamic Contrast-Enhanced MRI as a Noninvasive    Biomarker of Epidermal Growth Factor Receptor vIII Status. AJNR Am J    Neuroradiol. 2015; 36:2256-61.-   45. Gevaert O, Mitchell L A, Achrol A S, Xu J, Echegaray S,    Steinberg G K, et al. Glioblastoma multiforme: exploratory    radiogenomic analysis by using quantitative image features.    Radiology. 2014; 273:168-74.-   46. Ellingson B M. Radiogenomics and imaging phenotypes in    glioblastoma: novel observations and correlation with molecular    characteristics. Curr Neurol Neurosci Rep. 2015; 15:506.-   47. Batmanghelich N, Dalca A, Quon G, Sabuncu M, Golland P.    Probabilistic Modeling of Imaging, Genetics and Diagnosis. IEEE    Trans Med Imaging. 2016; Epub ahead of print.-   48. Johnson B E, Mazor T, Hong C, Barnes M, Aihara K, McLean C Y, et    al. Mutational Analysis Reveals the Origin and Therapy-Driven    Evolution of Recurrent Glioma. Science. 2014; 343:189-93.-   49. Vecchio C A D, Giacomini C P, Vogel H, Jensen K C, Florio T,    Merlo A, et al. EGFRvIII gene rearrangement is an early event in    glioblastoma tumorigenesis and expression defines a hierarchy    modulated by epigenetic mechanisms. Oncogene. 2013; 32:2670-81.-   50. Gill B J, Pisapia D J, Malone H R, Goldstein H, Lei L, Sonabend    A, et al. MRI-localized biopsies reveal subtype-specific differences    in molecular and cellular composition at the margins of    glioblastoma. Proc Natl Acad Sci USA. 2014; 111:12550-5.

All publications cited in this specification, including thosespecifically recited above, are incorporated herein by reference. Alsoincorporated by reference herein is U.S. Provisional Patent ApplicationNo. 62/484,034, filed Apr. 11, 2017 and US Provisional PatentApplication No. 62/325,764, filed Apr. 21, 2016, and the code atwww.med.upenn.edu/sbia/-phiestimator.html.

While the principles of the invention have been described above inconnection with specific devices, apparatus, systems, algorithms, and/ormethods, it is to be clearly understood that this description is madeonly by way of example and not as limitation. One of ordinary skill inthe art will appreciate that various modifications and changes can bemade without departing from the scope of the claims below.

The above description illustrates various embodiments along withexamples of how aspects of particular embodiments may be implemented,and are presented to illustrate the flexibility and advantages ofparticular embodiments as defined by the following claims, and shouldnot be deemed to be the only embodiments. One of ordinary skill in theart will appreciate that based on the above disclosure and the followingclaims, other arrangements, embodiments, implementations and equivalentsmay be employed without departing from the scope hereof as defined bythe claims. Accordingly, the specification and figures are to beregarded in an illustrative rather than a restrictive sense, and allsuch modifications are intended to be included within the scope of thepresent invention. The benefits, advantages, solutions to problems, andany element(s) that may cause any benefit, advantage, or solution tooccur or become more pronounced are not to be construed as a critical,required, or essential features or elements of any or all the claims.The invention is defined solely by the appended claims including anyamendments made during the pendency of this application and allequivalents of those claims as issued.

We claim:
 1. A computer-implemented method for in vivo detection of anepidermal growth factor receptor (EGFR) mutation status withinperitumoral edematous tissue, comprising executing on a processor thesteps of: performing quantitative pattern analysis of magnetic resonanceimaging (MRI) data corresponding to MRI of in vivo peritumoral edematoustissue to determine a level of spatial heterogeneity or similaritywithin the in vivo peritumoral edematous tissue; and assigning EGFRmutation status as one of negative or positive based on the level ofspatial heterogeneity or similarity determined during said performingstep.
 2. The method according to claim 1, wherein the mutation isselected from EGFR variant III (vIII), a point mutation at EGFR A289V, apoint mutation at EGFR variant G598, and/or a point mutation at EGFRvariant R108, with reference to the residue numbering of SEQ ID NO:1. 3.The computer-implemented method according to claim 1, wherein, duringsaid performing step, spatial heterogeneity or similarity of perfusiontemporal dynamics is determined within the in vivo peritumoral edematoustissue.
 4. The computer-implemented method according to claim 1,wherein, during said performing step, imaging data of at least separatefirst and second regions of interest (ROIs) within the in vivoperitumoral edematous tissue are analyzed and compared to determine thelevel of spatial heterogeneity or similarity therebetween.
 5. Thecomputer-implemented method according to claim 3, wherein said first ROIwithin the in vivo peritumoral edematous tissue corresponds to a regionof tissue adjacent an enhancing part of a tumor, and wherein said secondROI corresponds to a separate region of tissue within the in vivoperitumoral edematous tissue located at a location spaced farthest fromthe enhancing part of the tumor along a periphery of the in vivoperitumoral edematous tissue.
 6. The computer-implemented methodaccording to claim 5, wherein said performing step includes applying amulti-variance statistical procedure to the MRI data to determineperfusion temporal dynamics of the first and second ROIs.
 7. Thecomputer-implemented method according to claim 6, wherein the MRI datais dynamic susceptibility contrast material-enhanced magnetic resonanceimaging (DSC-MRI).
 8. The computer-implemented method according to claim6, wherein said multi-variance statistical procedure is PrincipalComponent Analysis (PCA).
 9. The computer-implemented method accordingto claim 6, wherein said performing step includes measuring separabilitybetween the perfusion temporal dynamics determined for the first andsecond ROIs, and wherein said assigning step includes an assignment ofEGFR-positive status when the separability is low and an assignment ofEGFR-negative status when the separability is high.
 10. Thecomputer-implemented method according to claim 9, wherein separabilityis measured via Bhattacharyya coefficient analysis.
 11. Thecomputer-implemented method according to claim 1, wherein the MRI datais selected from the group consisting of dynamic susceptibility contrastmaterial-enhanced magnetic resonance imaging (DSC-MRI) data, dynamiccontrast enhanced (DCE) MRI perfusion image data, T1-weighted (pre- andpost-contrast) data, T2-weighted (pre- and post-contrast) data, andT2-weighted fluid-attenuated inversion recovery (T2-FLAIR) data.
 12. Amethod of in vivo detection of epidermal growth factor receptor (EGFR)mutation status within peritumoral edematous tissue of a patient,comprising the steps of: acquiring MRI data corresponding to in vivoperitumoral edematous tissue of a patient; identifying separate,non-overlapping first and second ROIs within the peritumoral edematoustissue; analyzing the MRI data corresponding to the separate first andsecond ROIs to determine a level of heterogeneity or similaritytherebetween; and assigning EGFR mutation status as one of negative orpositive based on the level of heterogeneity or similarity determinedduring said analyzing step.
 13. The method according to claim 12,wherein the mutation is selected from EGFR variant III (vIII), a EGFRvariant at position A289, a point mutation at EGFR G598V, and/or a pointmutation of EGFR variant position R108, with reference to the residuenumbering of SEQ ID NO:1.
 14. The method according to claim 12, whereinsaid first ROI within the peritumoral edematous tissue corresponds to aregion of tissue adjacent an enhancing part of a tumor, and wherein saidsecond ROI corresponds to a separate region of tissue within theperitumoral edematous tissue located at a location spaced farthest fromthe enhancing part of the tumor along a periphery of the peritumoraledematous tissue.
 15. The method according to claim 14, wherein saidfirst ROI is defined on a contrast-enhanced T1-weighted (T1-CE) MRIduring said identifying, and wherein said second ROI is defined on aT2-weighted fluid-attenuated inversion recovery (T2-FLAIR) MRI duringsaid identifying step.
 16. The method according to claim 12, wherein,during said analyzing step, heterogeneity or similarity of perfusiontemporal dynamics between said first and second ROIs is determined via atime-series of MRI data.
 17. The method according to claim 16, whereindata from dynamic susceptibility contrast material-enhanced magneticresonance imaging (DSC-MRI) is used during said analyzing step todetermine heterogeneity or similarity of perfusion temporal dynamicsbetween said first and second ROIs.
 18. The method according to claim16, wherein said analyzing step includes applying a multi-variancestatistical procedure to the MRI data to determine perfusion temporaldynamics of the first and second ROIs.
 19. The method according to claim18, wherein said multi-variance statistical procedure is PrincipalComponent Analysis (PCA).
 20. The method according to claim 18, whereinsaid analyzing step includes measuring separability between theperfusion temporal dynamics determined for the first and second ROIs,and wherein said assigning step includes an assignment of EGFR-positivemutation status when the separability is low and indicates that theperfusion temporal dynamics between the first and second ROIs aresimilar and an assignment of EGFR-negative mutation status when theseparability is high and indicates that perfusion temporal dynamicsbetween the first and second ROIs are heterogeneous.
 21. The methodaccording to claim 20, wherein the separability is measured byBhattacharyya coefficient analysis.
 22. The method according to claim14, wherein, during said assigning step, EGFR-negative mutation statusis assigned when differing levels of neovascularization is determined toexist between said first and second ROIs and EGFR-positive status isassigned when similar levels of neovascularization is determined toexist in said first and second ROIs.
 23. A non-transitorycomputer-readable storage medium comprising stored instructions which,when executed by one or more computer processors, cause the one or morecomputer processors to perform steps of: performing quantitative patternanalysis of MRI data corresponding to MRI of peritumoral edematoustissue to determine a level of spatial heterogeneity or similaritywithin the peritumoral edematous tissue; and assigning EGFR mutationstatus as one of negative or positive based on the level of spatialheterogeneity or similarity determined during said analyzing step.
 24. Asystem for in vivo detection of epidermal growth factor receptor (EGFR)mutation status within peritumoral edematous tissue of a patient,comprising: at least one processor configured to perform quantitativepattern analysis of MRI data corresponding to MRI of in vivo peritumoraledematous tissue to determine a level of spatial heterogeneity orsimilarity within the peritumoral edematous tissue; and said at leastone processor being configured to assign EGFR mutation status as one ofnegative or positive based on the level of spatial heterogeneity orsimilarity determined.
 25. A method for targeted treatment of a patienthaving a neoplasm associated with an epidermal growth factor receptor(EGFR) mutation, the method comprising: (a) detecting an epidermalgrowth factor receptor variant (EGFR) mutation status within peritumoraledematous tissue of a patient according to the method of claim 1; and(b) treating a patient with a EGFR-targeting therapy.